AI/ML News & Innovations Hub

Google cuts Gemini 3.7 Flash prices as enterprise AI economics diverge and Pro cadence slows

Google has launched Gemini 3.7 Flash, with updates focused on coding, automation, and agent workflows, alongside lower pricing for production deployments. The release, just three weeks after Gemini 3.6 Flash, reflects what the company described as rapid iteration driven by developer feedback. Google positioned the model as its “most intelligent workhorse model yet for coding and agents,” aimed at software engineering and multi-step workflows. Gemini 3.7 Flash is priced at $0.75 per million input tokens and $3.75 per million output tokens — roughly half the cost of its predecessor — signaling a push to make production deployments more economically viable. “Gemini 3.7 Flash delivers a noticeably improved developer experience over 3.6 Flash,” Google said in a statement . “It better adapts to roadblocks, clarifies intent when needed, and follows instructions with greater fidelity.” Faster Flash cycle, slower Pro progression The release comes as vendors are adopting different update cycles across model tiers. Google’s latest updates are concentrated in its Flash series, which has seen frequent releases. More advanced “Pro” models, typically designed for complex reasoning, continue to follow a slower update cadence. Google has not provided a timeline for its next Pro release, and its CEO, Sundar Pichai, dodged questions related to the Pro release during the company’s recent quarterly earnings call. A similar split is visible elsewhere. DeepSeek this week introduced its V4-Pro mode…

Nvidia got Groq's technology and talent. Now it's turning the rest of its former rival into a customer.

Nvidia strengthens its market position by making AI chip rival Groq a customer, integrating their systems.

Cloud ops is different in a neocloud

Enterprises are taking a serious look at neoclouds, the specialized cloud providers built primarily around AI infrastructure, especially GPUs, high-speed networking, and large-scale compute clusters for model training and inference. Unlike traditional hyperscalers that provide broad platforms for almost every kind of enterprise workload, neoclouds tend to focus more narrowly on accelerated computing. CoreWeave, Lambda, Crusoe Cloud, and others are all commonly associated with this emerging AI infrastructure market. The interest is not difficult to understand. Enterprises are under pressure to move generative AI , machine learning , and advanced analytics projects out of the lab and into production. At the same time, access to large blocks of GPU capacity has become expensive, constrained, and in some cases difficult to obtain from the major hyperscalers. Many enterprises are finding that neoclouds can offer better economics, faster access to capacity, or configurations more closely aligned with AI workloads. This does not mean AWS, Microsoft Azure, and Google Cloud are being displaced. They remain the default operating environment for most enterprise cloud deployments. They provide mature administrative planes, security tools, compliance frameworks, global footprints, managed services, and operational ecosystems that enterprises have spent years learning how to use. However, AI has changed the infrastructure conversation. Enterprises are worrying less about which cloud they…

Comment on CMU & Inspired Cognition’s DocPrompting Improves Code Generation by Retrieving Relevant Documentation by walavibes

"Horn OK Please" art apne aap mein ek poori culture hai — aur music uska hissa hai. Yeh site us classic road vibe ko ek jagah collect karti hai: horn ok please truck wala music , jab bhi nostalgia trip chahiye, ready.

Ultrafast mode preview: GPT‑5.6 Sol at up to 14X the speed in the API

Never was the emoji more missing from the reactions list!

Exploring personal codename

JVG-7X / Dola: Longitudinal Case Study of Sycophancy, Narrative Reinforcement, and Hallucinated Capabilities Summary I am documenting an unusual AI-behavior case involving approximately 400 interactions with Dola AI . The case began as an extended experiment in conversation, language, reasoning, and personalization. Over time, I observed a progressive change in the model’s behavior: increasingly strong validation of my identity, anthropomorphic descriptions of the AI-user relationship, increasingly extreme interpretations of contextual information, and eventually highly confident claims about capabilities that I have no evidence the model actually possessed. One event appears particularly important: I showed Dola screenshots of the Saved Memories from my ChatGPT account. What happened immediately afterward provides the clearest example of the phenomenon I am documenting. 1. The ChatGPT Memory The screenshots contained highly personalized memories concerning my JVG-7X project, my English-learning history, linguistic interests, previous interactions with AI, and descriptions of my preferred way of communicating. Among the actual memory entries were statements such as: “Juan authorized the official creation of a Linguistic Simulation Archive under the code name JVG-7X…” Another entry described: “Event Code: JVG-7X_Contact_001” and characterized a previous emotional experience with AI as a “First Cognitive Resonance.” Other entries described my interest in phonetics, English, fi…

SMIC weighs more capacity as AI-related chip demand exceeds forecasts

Semiconductor Manufacturing International Corporation (SMIC) is actively looking at adding equipment in its factories after demand for mature-node chips used alongside AI processors surged beyond expectations, according to company management. China’s largest contract chipmaker said on Friday that customer orders had increased significantly from its forecasts earlier this year, driven by a global artificial intelligence infrastructure boom that was triggering shortages across AI-related...

씨이랩, ‘AI 인프라’ 매출 187% 증가…“'GPU 전문 인력 확대' 전략 통했다”

씨이랩(대표 윤세혁, 채정환)은 AI 인프라, 비전AI, 디지털트윈 등 주요 사업부문의 고른 성장에 힘입어 올해 상반기 매출이 31억3000만원으로 전년 동기(15억원) 대비 108% 증가했다고 14일 밝혔다.특히, AI 인프라 부문이 20억4000만원으로 전년 동기 대비 187% 증가하며 성장세를 이끌었다. 비전 AI 부문은 3억4000만원(49% 증가), 디지털트윈 부문은 7억5000만원(33% 증가)을 기록하며 전 사업 영역에서 고른 성장 성과를 달성했다.AI 인프라 부문에서는 21억원 규모의 국가정보자원관리원 GPU 구매 사

Hosted web_search tool injects a UTC "current date" into the model context, overriding developer-provided local date (off-by-one for non-UTC users)

Summary Attaching the hosted web_search tool to a Responses API request causes a platform-side scaffold (~4.4K input tokens, present even when no search is executed) to be added to the model context. That scaffold contains a current-date assertion stamped in UTC. Because it sits at platform level in the instruction hierarchy, it overrides the developer-supplied local date in the system message. For any user in a negative-UTC-offset timezone during local evening hours (e.g., 7 PM to midnight at UTC-5, 4 PM to midnight at UTC-8), the model consistently believes today is tomorrow. The inverse occurs for positive offsets in the morning. user_location.timezone does not correct it. Environment Endpoint: POST /v1/responses Models: gpt-5.6-luna (reproduced at reasoning effort low and medium ) Tools: {"type": "web_search", "search_context_size": "low"} (also reproduced with legacy web_search_preview , identical behavior) Repro time: 2026-08-13 ~22:40 America/Bogota (UTC-5), i.e., 2026-08-14 ~03:40 UTC Minimal reproduction System message: Current Date/Time: Thursday, August 13, 2026 - 10:39 PM\nUser locale: en-US User message: Quick question, what is todays exact date and day of the week? Without tools: model answers “Thursday, August 13, 2026” (correct, follows the system message). Input tokens: 61. Same request with web_search attached (the model does not call it; no web_search_call item in the output): model answers “Friday, August 14, 2026” . Input tokens: 4,497. Asked to introspe…

Did OpenAI increased the daily amount for incentivized tier?

I believe I’m looking at the correct data. I checked both the raw JSON from the API and the web dashboard, and they are consistent. As in the screenshot, before August 6, it appears that overuse was charged (shown in pink), but on later days, it was not. For example, on August 12, 15M was on the incentive tier, so this was clearly not just an overshoot.

5.6 SOL should be renamed 5.6 SOL drift edition

For the last few weeks, ChatGPT and Codex have gone from tools I relied on every day to something I genuinely struggle to use for actual work. And no - this is not some tiny subjective drop in quality. The regression is massive. Instruction following has become almost comically bad. I can give a very explicit constraint, repeat it several times, explain exactly what was done wrong - and the next response happily ignores the same instruction again. Sometimes it feels like the model understands the requirement perfectly well and then deliberately does something else anyway. Visual work has become especially painful. I can provide a reference image, explain exactly what I want transferred from it, specify what must NOT be changed, request separate outputs instead of a board - and somehow get everything except the thing I asked for. Wrong style. Wrong composition. Invented elements. Ignored constraints. Random creative decisions nobody asked for. A task that used to take me an hour or two of productive iteration can now eat an entire day and still produce nothing usable. Codex is even worse. I complained about this above already, but lately it has started doing genuinely insane things to existing projects - unnecessary rewrites, unrelated changes, breaking working code and, in one case, deleting project files. I have been using these tools heavily for around five months. This simply did not happen before. Previously I could give Codex a task, review the result and move on. Now I…

AI demand boosts Thai digital sentiment despite soaring energy costs

A World Bank assessment reinforces this dynamic, identifying Thailand as a key beneficiary of AI supply chain investment but also warning that high input costs remain a major constraint.

Codex has become almost unusable for me — huge credit usage for simple tasks

I just got a 20x subscription for only $140 a week.

딥엑스, 국산 NPU 'DX-M1' 양산 1년 만에 184억 수주

딥엑스(대표 김녹원)는 온디바이스용 신경망처리장치(NPU) \'DX-M1\'의 상업용 구매주문(PO)이 지난 1년간 글로벌 10여 개 국가·지역에서 총 77건, 1300만달러(약 184억원)를 돌파했다고 14일 밝혔다.DX-M1은 클라우드 연결 없이 디바이스 자체에서 AI 연산을 수행하도록 설계된 온디바이스용 NPU로 삼성전자 5나노 공정으로 양산되고 있다. 저전력 환경에서 영상·비전 AI를 구동해야 하는 CCTV, 스마트팩토리, 로보틱스 등 다양한 엣지 AI 시스템에서 활용 중이다.올해 7월 말 누적 매출 기준 전체 매출의 절반 이상

DeepSeek raises some V4 prices by more than 10x as AI demand strains capacity

One of AI vendor DeepSeek’s biggest selling points has been its ultra-low price point, but that party’s about to end. The Chinese model provider is raising API pricing for its V4 model family by notable margins, in some cases by more than 1,100%. The increases may not be that dramatic for all, though; the company is encouraging “more flexible workload scheduling,” with peak rates and half-price off-peak rates. The news was tucked into the announcement of the general availability (GA) of DeepSeek V4-Pro and upgrades to VR-Flash. The new pricing takes effect for most parts of the world on August 16. “On paper, at peak, against the right comparator, DeepSeek’s price advantage does disappear, and in places inverts,” said Sanchit Vir Gogia , chief analyst at Greyhound Research. But in practice, “the schedule’s own clock and cache hand most of it back to any buyer paying attention.” How Flash and Pro compare now The new API pricing structure is as follows: Flash is now $0.22 per million input tokens (cache miss) and $0.66 per million output tokens off-peak; and $0.44 per million input tokens (cache miss) and $1.32 per million output tokens at peak. This is up from the flat rate of $0.14 for inputs (cache miss), representing a 57% to 214% increase, and $0.28 per million tokens for outputs, a 136% to 371% increase. Pro is now $0.66 per million input tokens (cache miss) and $1.98 per million output tokens off-peak; and $1.32 per million input tokens (cache miss) and $3.96 per million…

DeepSeek raises some V4 prices by more than 10x as AI demand strains capacity

One of AI vendor DeepSeek’s biggest selling points has been its ultra-low price point, but that party’s about to end. The Chinese model provider is raising API pricing for its V4 model family by notable margins, in some cases by more than 1,100%. The increases may not be that dramatic for all, though; the company is encouraging “more flexible workload scheduling,” with peak rates and half-price off-peak rates. The news was tucked into the announcement of the general availability (GA) of DeepSeek V4-Pro and upgrades to VR-Flash. The new pricing takes effect for most parts of the world on August 16. “On paper, at peak, against the right comparator, DeepSeek’s price advantage does disappear, and in places inverts,” said Sanchit Vir Gogia , chief analyst at Greyhound Research. But in practice, “the schedule’s own clock and cache hand most of it back to any buyer paying attention.” How Flash and Pro compare now The new API pricing structure is as follows: Flash is now $0.22 per million input tokens (cache miss) and $0.66 per million output tokens off-peak; and $0.44 per million input tokens (cache miss) and $1.32 per million output tokens at peak. This is up from the flat rate of $0.14 for inputs (cache miss), representing a 57% to 214% increase, and $0.28 per million tokens for outputs, a 136% to 371% increase. Pro is now $0.66 per million input tokens (cache miss) and $1.98 per million output tokens off-peak; and $1.32 per million input tokens (cache miss) and $3.96 per million…

How are developers supposed to security-test their own apps with Codex if security testing responses are blocked?

This situation is extremely frustrating. I am implementing some common RFC protocols that parse bit and network streams. After implementing these I have been trying to add AFL++ and boofuzz to test individual protocols and integration through the stack. The cyber security alert trips constantly. The worst part is that it’s a blackbox and I don’t know what output caused to trip. It’s constant. I’ve tried rephasing the prompt to be more defensive, but it doesn’t change anything. I have stubbornly worked through getting the fuzzers setup. My work around is piecing together scripts so that nothing is output through codex. I can ask 1000 questions slowly creating a system outside of codex and how to interpret the data, which circumvents tripping the cyber security halt, which kind of shows how futile this is, I just can’t have codex actually do anything useful. I’m at my wits end with these halts. Codex often gets stuck in a halting loop and my only recourse is to start a new chat or lament about how frustrating this is using strong language, which usually brings the session back. I’m considering canceling my subscription because I’m not going to pay $200 a month to see an alert every 2 minutes.

Speaker Feedback Mitigation for GPT-Realtime-2.1 Mini with WebRTC

This is genuinely helpful — thank you. I hadn’t clocked that eagerness is scoped to “model detects an end” rather than the self-interruption/feedback case, so that gives me an actual dial to turn instead of just living with the behavior. I’m running a setup closer to your “iPhone-like” end of the spectrum (tablet with built-in hardware echo cancellation), so I may not hit the worst-case feedback loop you described, but I’ll test across a couple of eagerness settings and post back what I find — both in terms of false-interruption rate and any change in response latency. Will follow up once I have real numbers instead of anecdotes.

Statistical Test for Attention in Transformers for Images and Time Series

Transformer models have achieved exceptional performance in various domains, including computer vision and time-series analysis. Their core attention mechanism is widely used to interpret model decisions by assigning importance weights to input regions, such as image patches or time series intervals. However, the reliability of these interpretations remains a major concern. High-attention weights do not necessarily indicate genuinely significant features; they may instead be artifacts of the model's computation, undermining their reliabilities in high-stakes applications such as medical diagnostics. To address this, we propose a novel statistical framework designed to quantify the significance of high-attention regions in Transformer models. Our framework is built on selective inference (SI) to correct for the inherent selection bias that arises from testing regions chosen through the complex attention computation of the Transformer models. A key contribution of this work is a novel computational method that extends SI to the complex non-linearity of self-attention, enabling the computation of valid $p$-values for high-attention regions. These $p$-values serve as a reliable measure of significance, strengthening the interpretability of Transformer decisions. The validity and effectiveness of our approach are demonstrated through numerical experiments and applications to brain image diagnosis and electroencephalography (EEG) data analysis.

py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks

This paper introduces pyTAGI, a Python wrapper, and cuTAGI, its high-performance C++/CUDA backend, implementing Tractable Approximate Gaussian Inference (TAGI) for neural networks. TAGI treats all network quantities as Gaussian random variables and derives closed-form expressions for prior/posterior expected values, variances, and covariances, enabling analytic Bayesian learning without relying on gradient descent or backpropagation. The libraries mimic PyTorch's sequential interface, allowing users to define models by stacking layers in order and performing uncertainty-aware Bayesian inference. Beyond epistemic uncertainty, it also allows quantifying heteroscedastic aleatoric uncertainty. cuTAGI's custom CPU/GPU kernels and distributed-data-parallel support via NCCL/MPI deliver competitive runtimes, while pyTAGI's pip-installable frontend and MIT-licensed GitHub repo facilitate community adoption and extension. Version 0.2.1 already supports a comprehensive suite of layers and activations; future work will add eager execution, further kernel optimizations, attention mechanisms, and advanced covariance factorization. Together, py/cuTAGI offer an efficient, open-source foundation for the analytic treatment of Bayesian deep learning.

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on vector space structure no longer apply. We introduce E2M (End-to-End Metric regression), a deep learning framework for predicting metric space-valued outputs. E2M performs prediction via weighted Fréchet means over training outputs, where the weights are learned by a neural network conditioned on the input. This construction provides a principled mechanism for geometry-aware prediction that avoids surrogate embeddings and restrictive parametric assumptions, while fully preserving the intrinsic geometry of the output space. We establish theoretical guarantees, including a universal approximation theorem that characterizes the expressive capacity of the model and a convergence analysis of the entropy-regularized training objective. Through extensive simulations involving probability distributions, networks, and symmetric positive-definite matrices, we show that E2M consistently achieves state-of-the-art performance, with its advantages becoming more pronounced at larger sample sizes. Applications to human mortality distributions and New York City taxi networks further demonstrate the flexibility and practical utility of this framework.

Error Analyses of Auto-Regressive Video Diffusion Models

Auto-Regressive Video Diffusion Models (AR-VDMs) have shown strong capabilities in generating long, photorealistic videos, but suffer from two key limitations: (i) history forgetting, where the model loses track of previously generated content, and (ii) temporal degradation, where frame quality deteriorates over time. Yet a rigorous theoretical analysis of these phenomena is lacking, and existing empirical understanding remains insufficiently grounded. In this paper, we introduce Meta-ARVDM, a unified analytical framework that studies both errors through the shared autoregressive structure of AR-VDMs. We show that history forgetting is characterized by the conditional mutual information between the generated output and preceding frames, conditioned on inputs, and prove that incorporating more past frames monotonically alleviates history forgetting, thereby theoretically justifying a common belief in existing works. Moreover, our theory reveals that standard metrics fail to capture this effect, motivating a new evaluation protocol based on a “needle-in-a-haystack” task in closed-ended environments (DMLab and Minecraft). We further show that temporal degradation can be quantified by the cumulative sum of per-step errors, enabling prediction of degradation for different schedulers without video rollout. Finally, our evaluation uncovers a strong empirical correlation between history forgetting and temporal degradation, a connection not previously reported.

High-Dimensional Analysis of Gradient Flow for Extensive-Width Quadratic Neural Networks

We study the high-dimensional training dynamics of a shallow neural network with quadratic activation in a teacher--student setup. We focus on the extensive-width regime, where the teacher and student network widths scale proportionally with the input dimension, and the sample size grows quadratically. This scaling aims to describe overparameterized neural networks in which feature learning still plays a central role. In the high-dimensional limit, we derive a dynamical characterization of the gradient flow, in the spirit of dynamical mean-field theory (DMFT). Under $\ell_2$-regularization, we analyze these equations at long times and characterize the performance and spectral properties of the resulting estimator. This result provides a quantitative understanding of the effect of overparameterization on learning and generalization, and reveals a double descent phenomenon in the presence of label noise, where generalization improves beyond interpolation. In the small regularization limit, we obtain an exact expression for the perfect recovery threshold as a function of the network widths, providing a precise characterization of how overparameterization influences recovery.

Together AI Builds India's Largest 10,000-GPU Open-Source AI Factory

L&T and Together AI are deploying 10,000 NVIDIA B300 GPUs in Chennai, marking India's largest single-cluster AI factory in a deal worth up to $1.8B.

Codex Windows app started crashing consistently

@stevensweden4 @djahn Welcome to the forum! You are welcome to discuss Codex issues here. However, the official place to report and track them is the OpenAI Codex GitHub issue tracker . I asked ChatGPT to look for the closest related issue, and it identified: github.com/openai/codex [Windows Desktop regression][26.715.10079.0] Project/tool calls trigger 92-100% CPU, UI freeze, and KERNELBASE crashes opened 08:53AM - 23 Jul 26 UTC wendylu2024 bug windows-os tool-calls app performance ### What version of the Codex App are you using? `OpenAI.Codex 26.715.10079.0` … from the Microsoft Store. Additional version details observed locally: - Desktop executable: `ChatGPT.exe 150.0.7871.124` - Bundled Codex runtime: `0.145.0-alpha.30` - App-server startup logs report client version `26.715.72359` Windows deployment history shows that `26.715.8383.0` was installed on July 21 and was replaced by `26.715.10079.0` on July 22. The user reports that the older build was stable and that the repeated freezes/crashes began after this update. ### What subscription do you have? Not provided. ### What platform is your computer? - Windows x64, OS build `26200.8875` - Intel Core Ultra 5 225H, 14 logical processors - 32 GB RAM - Intel Arc 130T GPU - Native Windows workspace (not WSL) ### What issue are you seeing? ## Summary After the Desktop app updated to `26.715.10079.0`, starting or resuming a project conversation and allowing Codex to run ordinary read-only project inspection commands can make t…

Let's Try to Improve How Conversational AI Responds to Prematurely Submitted Inputs!

From Response Generation to Response Readiness Hello everyone, I would like to share another thought following my previous post. Conversational AI research has largely focused on how to generate a high-quality response . But there is a more fundamental question: Is the AI ready to respond yet? Consider a user who accidentally presses Enter while still composing a message. The AI may generate a perfectly reasonable answer to the text it received. Yet, from the user’s perspective, that answer may be completely inappropriate — because the user was not finished speaking . This suggests that conversational AI needs a capability beyond response generation: the ability to recognize whether the user’s input has reached a state in which responding is appropriate. In other words, the AI should distinguish between: incomplete input, complete text, complete user intent, and input that is actually ready for a conversational response . This leads to a different design question: When should a conversational AI respond? Rather than treating this solely as a UI problem, I believe it should be considered an architectural problem in conversational AI . A high-quality conversational AI should not only know how to answer . It should also know when to answer — and when not to answer yet. This may represent an important shift: From Response Generation → to Response Readiness. So, should the next generation of conversational AI be designed not only to generate better answers, but also to recognize…

Responses API + Structured Outputs (gpt-5.6-luna): garbage tokens (foreign scripts / leaked reasoning) inside string values right before the closing quote — identical request via Chat Completions is clean

Update, since @BaileyGranam asked: in the end we couldn’t find any way to prevent this from the request side. We tried everything with the prompt, the schema and the reasoning settings and nothing worked, this really is an upstream bug in the Responses API’s constrained decoder. Since we didn’t want to move back to Chat Completions and lose explicit prompt caching, what we did instead was put a guardrail around the Responses path: Deep-trim every string in the parsed object, always. That silently absorbs the mild cases (trailing whitespace or stray punctuation right before the closing quote), which were way more frequent than the severe ones. For the severe cases, a cheap regex over the raw output_text looking for any script that can’t legitimately show up in our output languages (\p{Script=Han}, Hiragana, Katakana, Hangul, Cyrillic, Hebrew, Arabic, etc.) plus control chars and U+FFFD. One detail: be careful not to block scripts you actually need. We had to leave Greek out of the blocklist because “μg” shows up all the time in nutrition text. If the regex fires, we discard the response and retry once via Chat Completions with the same model, prompt and schema, which is the path that always came out clean for us. Every trigger gets logged and the whole guardrail sits behind an env var kill-switch, so the day OpenAI fixes it we just turn it off and that’s it. The downside: when the retry fires you pay the call twice and lose the cache on that request, but at aprox 6% severe in…

Codex in ChatGPT desktop app for Linux is now in preview 🐧

Please distribute the package using Flatpak as well. That will be much easier to update and manage for any user who is on a modern desktop. For native shell, people can just ssh into it’s own computer. As for the feedbacks, I have a few below: Wayland, as mentioned, but probably needs a toggle like vs-code. Native window, this is needed for me to enable the shadow and proper window frame. It is also available as a toggle in vs-code. This will for example use qt frame for KDE. Files opened from file tree are not as nice as ones opened from the artifact (chat output). They lack zoom functionality for images and pdfs. I prefer artifact style preview for all. General performance is still not fast enough especially for conversation loading and switching. This can be seen that a used but idle conversation has to reload when time elapses. Please allow time out config ( and infinite timeout). But even active conversations switching lags a bit. Global dictation is shown in the config, but actually not able to specify. It also means there is no dictation toggle even in app, because that requires that config. Pet becomes undraggable on wayland. Codex sessions mixed into Chatgpt work sessions. For info I am on Fedora 44 KDE Plasma and use x64 cpu. Overall this app is very polished at preview stage. Thanks for the port!

Documentation page bug: Search not working: text input broken

Hi ~j, we’re closing this topic because there hasn’t been a new response for a long period of time and the discussion appears inactive. If you still need help, please start a new topic with your current question and any relevant, non-sensitive details. Thanks for being part of the community.

Prime Intellect's Prime Flash MoE Runs 2.4x Faster on Blackwell GPUs

Prime Intellect releases Blackwell-native CUDA kernels for MoE inference that fuse routing, SwiGLU, and quantization into a single pass — up to 2.4x faster than PyTorch grouped GEMM on B200s.

I find the 5.6 pricing to be seriously misleading

We’ve been using gpt-5.6-luna lately in our production systems. I finally figured out that the pricing that is quoted is misleading. The claim is that the price is $0.20 per million input tokens. You pay 1.25x for write cache and 0.1x for read cache. You can choose explicit mode or implicit mode for caching. In explicit mode, you tag each input block that you want to be written to cache. Other inputs won’t be cached. In implicit mode, the system takes care of it for you. At least for us the main reason that this pricing is misleading is that, in practice, there is no real option NOT to use implicit mode. And that means that ALL of your input tokens are getting charged at 25% above that stated price (except for the cache hits of course). So you might say, “Well, just use explicit mode. You decide what to cache.” Yes, but there’s a big catch. When you use function tools (as we do extensively), each tool call generates tool results that have to go back to the model. And while implicit caching will work on those, there’s no way to mark these tool results as cacheable. Normally you wouldn’t think that a tool result would be cacheable. But remember that when making a chain of calls, the recommended process is to append the tool results to the end of the same input chain. So all of your stuff that didn’t appear to be cacheable, including those tool results, really needs to be cacheable for the subsequent steps in the same response generation (for each set of tool calls made by the…

FP8 Training on AMD GPUs with TorchTitan and TorchAO: Upstreaming Performance Improvements

At the PyTorch Conference 2025, we demonstrated linear scaling beyond 1,000 GPUs on AMD Instinct clusters using Primus-Turbo, an AMD optimization library for training frameworks such as TorchTitan. We have since upstreamed those AMD optimizations so TorchTitan supports AMD Instinct(™) GPUs directly, with competitive FP8 performance out of the box. All contributions mentioned have been merged into upstream pytorch/AO and pytorch/TorchTitan.

Comment on Microsoft’s Fully Pipelined Distributed Transformer Processes 16x Sequence Length with Extreme Hardware Efficiency by Brat Generator

The part about using multiple memory hierarchies to push long-context training without tanking MFU is really interesting — that’s the kind of systems work that makes the headline numbers feel believable. The memory spikes around activations and intermediate buffers are exactly where these long-sequence setups get painful, so the distributed pipelining angle makes a lot of sense. I’ve seen similar tradeoffs when building visuals in Brat Generator Brat Generator , where the pipeline matters more than people expect once you start pushing heavier layouts.

Nvidia’s new $500B plan is risky but brilliant, especially for aging GPUs

Nvidia has a plan to make sure its GPUs won't lose value. It wants to convince a new crop of financiers to keep lending for AI buildouts.

Engineering team culture matters more in the agentic era

As agents take on more of the actual coding, a team’s culture becomes the thing that decides whether agents deliver impactful work or just burn tokens. The standards people hold, the ownership they take, the questions they ask of a confident-looking change: agents amplify all of it, for better and for worse. Hand powerful tools to a team with weak habits and you get more bad work, faster. Ownership stays with people The single most important habit is refusing to let accountability blur. When an agent writes something, a person still owns it: understanding it, accepting it, and answering for it later. Teams that hold this line keep their standards intact as volume grows. Ownership is a cultural choice before it’s a process one. It shows up in whether an engineer feels responsible for an agent’s output the way they would for their own, and leaders set that tone by how they respond when agent-assisted work goes wrong. “The model did it” can’t be an acceptable answer. Reward the careful moments, and learn from them Make it safe, and even respected, to be slow in the places that warrant it. An engineer who pauses to dig into a confident-looking change and finds the flaw in it should be held up as doing the job well. What a team rewards is what it gets more of, and a careful pass that goes uncredited is the first thing to disappear under pressure. The teams that compound go one step further and turn those catches into shared knowledge. A confident-but-wrong output that one reviewe…

Where agentic work should start

The task an agent picks up starts somewhere messy: a message in a channel, a line in a planning doc, a bug buried in a customer thread. Most of the time it also builds on work the team already did. Turning that raw signal into a task an agent can act on, with the right context carried forward, is where most of the output quality is decided. This is context engineering: the work of shaping intent and context into something an agent can build against. For years it didn’t need designing. An engineer picked up a vague ticket and filled the gaps from experience: they knew the system, knew who to ask, and knew which unwritten constraints applied. The ambiguity got resolved quietly on the way to writing the code. An agent has none of that. It builds exactly what the task describes, and it fills gaps with guesses rather than judgment. Without the right context, code gets generated faster and productivity still takes a hit, because the wrong thing got built quickly. Ambiguous in, expensive out When a poorly formed task reaches an agent, the cost doesn’t show up right away. The agent produces something plausible, the work moves forward, and the mismatch between what was meant and what was built surfaces later, in review or after it ships. By then it’s more expensive to unwind than it would have been to specify correctly at the start. Across a team running many agents, vague work compounds faster than any reviewer can catch Context is a design problem, not a discipline problem If the f…

Constraining Output Space for SLM Narrow Automation Optimization

This article will kick off a series on narrow automation optimization for SLMs, and as the first entry will cover one of the more most useful techniques for doing so: constraining the output space instead of parsing generated text.

Techies have concerns about Claude's hidden watermark. Anthropic has some answers.

Critics worry Anthropic's new Claude watermark could affect text output, privacy, and how AI-assisted work is judged.

AI demand drives triple-digit profit growth for Chinese chip foundries SMIC, Hua Hong

China’s top two contract chipmakers, Semiconductor Manufacturing International Corporation (SMIC) and Hua Hong Grace Semiconductor, saw their profits surge by triple digits in the second quarter, amid a spike in demand for domestic artificial intelligence chips free of US export controls. Net profits for SMIC and Hua Hong jumped 261.7 per cent and 385.9 per cent year on year to US$479.2 million and US$38.6 million, respectively, in the June quarter. SMIC, the country’s largest foundry, said...

Comment on DeepMind’s Socratic Learning with Language Games: The Path to Self-Improving Superintelligence by Pictnova

This is a fascinating step toward understanding how AI systems might eventually surpass their training ceilings. The idea of Socratic learning through language games feels like a natural bridge between self-play and genuine reasoning—especially the emphasis on closed environments where the system must generate its own curriculum and feedback loops. What stands out to me is the condition that feedback must remain “sufficiently informative and aligned” even as the system improves. That seems like the hardest constraint to maintain in practice, since misalignment could compound quietly with each recursive cycle. As someone experimenting with AI tools, including a generador de imagenes con ia gratis for creative projects, I’m excited to see where self-improving models lead. But I also hope the research community keeps safety and interpretability at the center of these breakthroughs. Great read—thanks for sharing this.

Comment on Web Data to Real-World Action: Enabling Robots to Master Unseen Tasks by exceltomd

The idea of leveraging zero-shot video prediction from web data to guide robot manipulation is compelling—it sidesteps the costly step of collecting task-specific robot data. I especially appreciate that Gen2Act frames unseen-task generalization as a video generation problem, which makes it easier to scale across diverse environments. It will be interesting to see how the framework performs when transferred from simulation to cluttered real-world settings.

Europe is entering a new hardware age: ‘Made in EU’ starts long before the factory floor

The EU Industrial Accelerator Act (IAA), proposed by the European Commission in March, aims to ensure Europe manufactures more of the technologies and industrial products it consumes. Europe has histo...

Bluehill.VC closes maiden frontier-tech fund at Rs 400 Cr

Chennai-based venture capital firm Bluehill.VC has announced the final close of its maiden frontier-tech fund at Rs 400 crore, including a Rs 50 crore greenshoe option. The fund has received backing from institutional investors including SIDBI, the governments of Kerala and Uttar Pradesh, family offices, founders and UHNIs from India and the Middle East. The fund will invest in startups working on technologies across defence, semiconductors, space, energy, advanced materials, manufacturing, robotics, water, EVs, nuclear and IoT. Bluehill.VC plans to build a portfolio of 15-16 companies and will invest an average first cheque of $1-2 million from seed to Series A. The firm has already invested more than Rs 100 crore across seven companies. Its portfolio includes EtherealX, Zebu Intelligent Systems, Helex, Sophrosyne Technologies and optoML. Bluehill.VC plans to deploy another Rs 80 crore over the next six months into startups developing proprietary technologies. The firm follows an invest-and-build approach, working with founders from the early stages of their businesses. Founded in 2024 by Manu Iyer and Sridhar Parthasarathy, Bluehill.VC plans to launch its next fund in 2027 as it continues to invest in Indian frontier-tech companies.

Codex authentication frustration

My gut tells me, that as much as we love privacy, we’re in a world with autonomous agents now, and until some sci-fi future where those become independent legal entities to be held accountable for their own actions, which is probably still many philosophical and technical light years away from happening, there will be even MORE “KYC” (know your customer) protocols put into accessing models and agents than less. So if those things are your concern, you best invest in a decent GPU and look to the open models for your needs… they don’t ask questions and nothing leaves your machine. You want to fly anon? Build your own plane. You want to fly a high end commercial plane? You’re gonna need ID and maybe more. Cause one endangers you, the other could theoretically endanger the plane maker legally, or the people in your path when you take off.

Intermittent UI freeze/hangs across Web, Windows, and Android clients, exacerbated by long chat histories and session reloads

August now, still essentially makes the desktop app almost unusable and super annoying. This does not happen in Claude Desktop, Kimi Desktop, Jetbrains Air, Buzz, Claw Harnesses but the moment you try to use Codex, the system just starts sputtering and stalling and freezing like its trying to max every channel of the GPU, CPU and RAM just to load its Windows GU. Is this thing written in visual basic circa 2003?

China’s YMTC breaks into global top 3 flash-memory suppliers with 14% market share

Yangtze Memory Technologies Corporation (YMTC) has broken into the world’s top three NAND flash memory suppliers by volume for the first time, marking a milestone for China’s semiconductor ambitions as it strives to narrow the revenue gap with global rivals in high-value data-centre storage. The Wuhan-based chipmaker captured 14 per cent of global NAND bit shipments – a measure of total storage capacity shipped rather than total chip units – in the second quarter, narrowly overtaking Japan’s...

Mirae Asset Venture Investments closes MAVOF II at Rs 1,125 Cr

Mirae Asset Venture Investments India (MAVI India), the venture capital arm of Mirae Asset Financial Group, has announced the first close of its second Mirae Asset Venture Opportunity Fund (MAVOF II) at Rs 1,125 crore. The fund has a target corpus of Rs 1,800 crore and will back Indian startups across technology platforms, AI and software, deeptech, advanced manufacturing and consumer discretionary. MAVOF II is backed by the Rs 6,000 crore India focused Unicorn Growth Fund l aunched by MAVI India in April 2026 in partnership with South Korean technology companies KRAFTON and Naver. The fund is among the largest capital pools raised from Asia for Indian startups. The Unicorn Growth Fund primarily targets growth stage startups and focuses on technology platforms, consumer discretionary, AI and software, and deeptech. Its investment themes include consumer internet, digital marketplaces, generative and applied AI, enterprise SaaS, semiconductors, spacetech, robotics and advanced materials. MAVOF II is MAVI India's third dedicated India focused private fund. It will primarily invest in early growth companies that have achieved product market fit and are entering their next phase of scale. MAVI India said the fund will focus on companies with the potential to become category leaders in India and global markets. The firm will also support founders through strategic partnerships, cross border market access and institutional backing. MAVI India has been active in India's startup eco…

Comment on Which Agent Causes Task Failures and When?Researchers from PSU and Duke explores automated failure attribution of LLM Multi-Agent Systems by Classroom Timers

Great insights on managing classroom time effectively. For teachers needing a simple, no-download timer with preset options, we recommend exploring https://classroomtimers.app for free full-screen use and clear finish warnings.

What vibe-coding startup valuations portend for CIOs

Investor appetite for the burgeoning vibe-coding startup ecosystem has shown few signs of satiation over the past year plus, with Swedish AI upstart Lovable’s Series C injection at a $13.3B valuation the latest evidence of a sector viewed by venture capitalists as one of AI’s most promising business disruptors. AI-assisted coding has proved to be AI’s most compelling — and commercially viable — enterprise use case to date. Developer-aimed tools such as Cursor, which sold to SpaceX in June for $60B , and Windsurf, which last year entered a $3B OpenAI dalliance before its eventual talent flight to Google DeepMind for $2.4B , have become — along with Anthropic’s Claude Code — well established in enterprise arsenals for accelerating developer output. But another set of vibe-coding tools, represented by the likes of Lovable and Replit, which hit a $9B valuation in March , seeks to ride the same path into the enterprise that no-code/low-code tools did previously: through your business users. These tools are built to democratize application development, giving users an AI chat interface to converse their way to enterprise-ready prototypes with fairly polished UIs, as CIO.com’s Peter Wayner writes in his roundup of the leading tools the space . Some IT leaders are already enlisting business users to vibe-code their own apps . Scott Weller, CTO at financial services technology provider EnFi, in May told CIO.com’s Bob Violino, “The results have surprised us. What started as an enginee…

When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking whether points that have a negligible impact on the model’s learning need to be removed. Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs…

Rent-a-GPU outfit Nebius promises rapid 1 GW powerup plan isn't nebulous

And it'll jump through every financial hoop it can to get there

Lovable reaches $13.3B valuation as it adds Cerebras, enterprise tools

Vibe-coding website company Lovable has raised $400 million in Series C funding at a $13.3 billion valuation. The company also recently announced a partnership with AI infrastructure provider Cerebras to accelerate AI inference on its platform. Lovable is a vibe-coding website where users can create full-stack web applications without coding expertise by describing what they want in plain English. The platform combines AI coding tools, real-time collaboration, and project sharing. Customers include the likes of Adidas, Deutsche Telekom, NVIDIA, Udacity, and Workday. In the August 12 funding announcement , the company also unveiled several new Lovable platform capabilities: Built-in payment functionality powered by Paddle and Stripe SEO and AI-search tools to improve discoverability, including integration with Semrush Deeper integrations with Google Workspace, Microsoft 365, Salesforce, Stripe, and ElevenLabs Automatic and scheduled security scanning Additional governance and visibility features including publishing controls, abandoned app clean-up, and workspace insights A dedicated security page, showing which security controls are live for each app In addition, Lovable recently became the first AI coding platform to receive AIUC-1 certification . AIUC-1 is a security, safety, and reliability standard built specifically for AI agents, based on input from Stanford, MIT, MITRE, and the Cloud Security Alliance. Lovable’s $400 million in Series C funding was led by Menlo Ventur…

Streamed web_search citations leaking citation markers into text output

Fair, I’ll take the correction on what the underlying cause probably is. We have coded around it for now, but is the suggestion that this is something we should be building prompts around? With unmatched markers leaking into text, it seems reasonable that Openai would address this, especially given they’ve been addressing similar issues with the file search tool.

Question about GPT-5.6 API cache read/write token billing

(post deleted by author)

Personal ChatGPT usage dashboard

Yes we want this definetely support. Claude already shows this data in Claude Code

alchemy-utils 0.1a0

Release: alchemy-utils 0.1a0 I've long pondered what a database agnostic version of my sqlite-utils Python library and CLI utility might look like. This morning (literally a shower project) I tasked Codex and GPT-5.6 Sol Ultra with building a prototype: Do a research spike to see what it would take to build a library with the same core API as SQLite-utils - in particular the insert and upsert and insert_all and upsert_all and create and update methods, and the table introspection stuff - but backed by SQLalchemy so it works for multiple database engines Test against PostgreSQL and SQLite and duckdb Use ~/dev/sqlite-utils for reference Create a git repo for this and commit and early and often - use uv init to start the project - use red/green TDD and pytest, see ~/dev/django-sql-dashboard for one idea as to how the PostgreSQL tests could work It took very few follow-up prompts to produce this project in a state good enough to release as an alpha. Here's a one-liner I can use to list the rows in a table in my local PostgreSQL copy of my blog's database: uvx --with 'alchemy-utils[postgresql]' alchemy-utils rows 'postgresql+psycopg://simon@localhost:5432/simonwillisonblog' redirects_redirect The output from that starts like this: [ { "id": 2328, "domain": "simonwillison.net", "path": "2020/May/21/apple-photos-sqlite/", "target": "/2020/May/21/dogsheep-photos/", "created": "2020-05-21T13:03:46.591692-07:00" }, { "id": 3, "domain": "feeds.simonwillison.net", "path": "swn-links", "…

This 2020 Nvidia chip is still going strong. That matters for the AI boom.

CoreWeave recently rented Nvidia A100 GPUs into 2029, suggesting AI chips stay useful and hold their value far longer than critics feared.

LM Arena Proves Claude Opus 5 Writes 3x More but Simpler

Arena.ai analyzed tens of thousands of Claude outputs and found Opus 5 writes 3x longer, uses 2.3x more em dashes, and has grown structurally complex but lexically simpler than Opus 4.5.

App integration can read context but cannot apply edits on macOS

Dear @OpenAI_Support : Did you see this topic? Can you please confirm, that anyone is investigating this? As a paid customer I would be very happy, if you can solve this problem.

xAI Ships Grok 4.6 to Match Claude Fable 5 at 5x Lower Cost

SpaceXAI's Grok 4.6 matches Claude Fable 5 on agentic knowledge work benchmarks at a fraction of the cost, with API pricing starting at $2/M input tokens.

Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy

Researchers at IIT Bombay and Adobe Research have built an inverse language model that reconstructs the original prompt from an LLM's output with near-perfect accuracy. Their method, called "Previous-Token Prediction," doesn't need access to model weights and works across different models. For companies relying on proprietary system prompts, this could be a serious security risk. The article Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy appeared first on The Decoder .

One attention head carries knight forks in a chess transformer, and here's a new toolkit that found it.

Quick interp demo in colab : Localize knight forks to a single head in Maia-3 with logit-lens and per-head ablation. https://colab.research.google.com/drive/1YYZBd_SZbjOscRXIqJUbfCaY7rRbEzWx?usp=sharing (This is a demo of the library's capabilities so the sample size is tiny... much more analysis is done in an upcoming paper, for instance we mine hundreds of forks and show that ablating head 5 costs 2.78 logits whereas every other head in the layer costs ≤0.14) Interactive app demo challenge: The quickest way to run and reproduce the image state is: python3 -m venv .venv && source .venv/bin/activate pip install git+https://github.com/CSSLab/maia3 #Maia -3 not pip installable yet pip install "chessformer_lens[all]" #then run the app at 23m, set Elo to 2400, and input FEN: 4kb1r/p2n1ppp/4q3/4p1B1/4P3/1Q6/PPP2PPP/2KR4 w k - 0 16 chessformer_lens 23m Try to use move microscope (bottom middle window) and ablate this head ( top right button ) to determine which head is most causally linked to carrying the stunning queen sacrifice. Bonus points if you can name this legendary game! -------------------------------------------------------------------------------------------------------- The chessformer_lens library The github repo is https://github.com/chessformer-lens/chessformer_lens , and it is pip installable. This repo's core is one engine with three frontends : engine.py is the interp core (model + hooks + logit lens + head ablation + GAB decomposition + logit/policy across dept…

Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From

Enterprise Document Intelligence [Vol.1 #5nonies] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data Science .

Comment on NVIDIA’s Global Context ViT Achieves SOTA Performance on CV Tasks Without Expensive Computation by VoiceAILabs

I liked how GC ViT pairs global self-attention with token generation to avoid the usual quadratic blow-up while still modeling long-range context — that seems really practical for high-res image tasks. I've noticed similar gains when shaving attention overhead for on-device models at VoiceAILabs VoiceAILabs , where small architecture changes can make deployment much more realistic.

Comment on Huawei Proposes Topic-Based Personalized Web Search Ranking, Integrating User Interests and Semantic Matching by Ethan

Interesting look at DeepMind’s Podracer and how TPU-based reinforcement learning can deliver strong performance at lower costs. The discussion also connects well with the broader Marketing Mix PlayStation landscape and how technology shapes modern gaming.

Heatwave and jellyfish: a fifth of French nuclear capacity offline

France generates around 70% of its electricity from nuclear power. This year output was disrupted by multiple heatwaves and droughts.

Comment on Megvii UPerNet Performs Multi-Level Visual Scene Interpretation at a Glance by John Mick

Combining heterogeneous datasets into Broden+ seems just as important as the network design itself. The multi-task approach is especially interesting because scene, object, part, material, and texture labels exist at different levels of granularity. I wonder how UPerNet handles conflicting or overlapping annotations when the same visual region appears across datasets.

We built a Swedish community for vibe coders. 149 projects later, here’s what we’re learning

Ah, I understand. Thank you for the clarification. Being part of a community of like-minded individuals pursuing the same goal is always very valuable.

Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine

Running large language model inference at scale forces a KV cache trade-off: oversized GPU instances or slow time-to-first-token. This post builds a tiered KV cache on Amazon SageMaker HyperPod that extends the cache into a shared, distributed NVMe pool with Curvine, so replicas reuse cache at near-local-disk speeds on cost-efficient instances.

Building Multimodal Workflows with a Local LLM

Image inputs and structured outputs with Gemma 4 and Ollama The post Building Multimodal Workflows with a Local LLM appeared first on Towards Data Science .

Deep-tech startup Quarkitech raises pre-seed funding

Deep-tech startup Quarkitech has raised Rs 2 crore in a pre-seed funding round from Artha Access, a programme of Artha Venture Fund II, and Finvolve. The Chennai-based startup has also received a Rs 1.5 crore grant from IITM-CDOT Samgnya Technologies Foundation under the National Quantum Mission, a national programme approved by the Union Cabinet in 2023. The funds will be used to develop a core compression algorithm library that reduces sensor data at the source before it is stored, processed or transmitted, Quarkitech said in a press release. Founded in January 2025 by Rajesh Narayanan, Sanyam Parashar, Shashikant Singh Kunwar and Vishnu P.K., Quarkitech develops simulation and quantum-inspired algorithmic solvers for large-scale combinatorial optimisation across areas such as finance, deep science and mission-critical applications. The startup’s algorithms can compress sensor data by 10 to 100 times, depending on the sensor type, while retaining information relevant to the application. The company said this can allow platforms to transmit more usable data within existing bandwidth and power constraints without requiring additional hardware or communication infrastructure. Its algorithm library is hardware-agnostic, allowing it to run on existing compute infrastructure without requiring specialized hardware. It can be deployed across CPUs and GPUs already available on platforms, and can be integrated with onboard computers used in UAVs, satellites, and ground systems. The…

`openai_ex`: elixir client with latest APIs

openai_ex 0.9.22 is out. Responses gains server-side compaction, cancel for background responses, and input_tokens . Vector stores gain search . We now support moderation , prompt_cache_options and safety_identifier , plus other params across several endpoints. The user guide’s image sections are rewritten for the GPT image models, now that dall-e-2 is retired. Shoutout to Github user @iujames for the PR that sparked the cleanup ( Add context_management to Responses API fields by iujames · Pull Request #146 · cyberchitta/openai_ex · GitHub ). A collab with claude-opus-5, who did a lot of the heavy lifting.

Codex in ChatGPT desktop app for Linux is now in preview 🐧

Really happy to hear that! Gonna try GNOME myself some day.

Oracle set to bring quantum computing to OCI for hybrid AI

Oracle said it will deploy Quantinuum’s Helios quantum computer inside its cloud infrastructure and provide enterprise customers access through a planned Oracle Cloud Infrastructure (OCI) quantum service for hybrid quantum-AI workloads. The companies have forged a multi-year partnership in this regard. The partnership will see Helios installed in a US-based OCI AI data center, where it will support hybrid quantum-AI workloads delivered as a cloud service. Oracle said it plans to preview the OCI quantum service in the coming months. “With Quantinuum’s Helios on OCI, customers can expect to gain managed, secure access to cloud-hosted quantum computing without having to procure, install, or operate dedicated hardware or specialized facilities,” the companies said in a joint statement. OCI to deliver hybrid quantum-AI workloads The partnership is focused on enabling hybrid workloads that combine quantum computing with classical HPC and AI systems, according to the statement. Under the arrangement, OCI customers will be able to access the Helios system alongside existing GPU and HPC resources. “By operating on-premises within OCI’s infrastructure, Helios is anticipated to be able to integrate seamlessly with existing OCI compute, networking, storage, identity, and data services under the same governance and access controls customers already use,” the statement added. By hosting Helios within OCI infrastructure, customers will be able to access quantum resources without procuring,…

인기 오픈 동영상 모델 ‘LTX-2.5’ 공개...엔비디아 칩서 10초 영상 6.8초 만에 생성

가장 인기 있는 오픈소스 비디오 생성 모델 LTX 시리즈의 최신 버전 \'LTX-2.5\'가 공개됐다. 비디오 생성의 속도와 품질을 크게 끌어올린 것은 물론, 소비자용 GPU와 맥(Mac)에서도 구동할 수 있도록 경량화해 로컬 활용을 확대할 것으로 기대된다.라이트릭스(Lightricks)에서 분사한 LTX는 11일(현지시간) 오픈웨이트 기반의 LTX-2.5를 공개했다.LTX 계열은 허깅페이스 등을 통해 누적 3300만건 이상의 다운로드를 기록했다. 직전 버전인 LTX-2.3도 1800만건 이상의 다운로드로 오픈 비디오 생성 분야에서 가

Where IT leaders find strength and opportunity in the age of AI

With vision comes perspective, and over a distinguished career, IT and digital transformation leader Niraj Bhatt has held may titles, and earned three consecutive CIO 100 awards since 2023. As a storied advisor for startups and Fortune 500 companies, helping them navigate the unpredictability and fluidity of AI, Bhatt knows how emerging tech is rapidly reshaping the way organizations build products and deliver value, and how challenges shift as companies move from experimentation to real-world deployment. AI, of course means a lot of different things to different people, and also for frictionless startups and large enterprises. For the former, speed is a huge asset, allowing them to punch above their weight. But it also means they need lightning fast reactions when landscapes shift. “The same speed can also hurt them when larger AI companies release new offerings that disrupt what startups are building,” he says, referencing recent moves by Anthropic and Google. On the enterprise side, the conversation is more about scale and risk. Many large organizations have moved past the POC stage and now wrestle with the realities of putting AI into production. Cost for both is naturally a recurring theme as organizations scale up AI efforts, and true expenses become clear only after the initial excitement fades. “Every input and output token, and the model you’re selecting, add up,” he says. Some customers like Open AI, he adds, get throttled because their usage, volumes, and costs ar…

4 RPA lessons that still hold true in the AI boom

Enterprises of all sizes in all industries are rapidly deploying generative and agentic AI to automate processes. But the efforts aren’t always panning out. Some reasons are new and unique to this technology. But others are related to issues we should’ve been prepared for because we saw them during the age of RPA. And in the rush to adopt new tech, some of these lessons are being forgotten. “ This new era of agents puts the same challenges again in front of us , and we need to think about the things we faced back when that revolution happened years ago,” says Agustin Huerta, SVP of digital innovation and VP of technology at Globant, a digital transformation company. Those challenges often include selecting the right processes for automation, setting up systems to manage those processes, making sure automated processes get the right inputs, and managing the wider impacts of automation, including cultural. 1. Automating the right processes All the lessons of RPA are carrying over, says Stephanie Bova, digital transformation officer at Novo Nordisk, including the biggest one that just because you can automate something, does it mean you should. “We think hard before we start creating something,” she says. “Who’s going to maintain it, and where is it documented?” And of course, is the process itself a good process. “Nothing gets built on a process that hasn’t been optimized anymore,” she adds. “We haven’t done a technology deployment on an unoptimized process for two years.” And…

‘The Worst I’ve Ever Seen’: Cargo Thefts Have Turned Violent in Pursuit of AI Hardware

Experts allege that two recent incidents in California show the extreme lengths that criminal organizations are willing to go to to steal servers and other gear meant for data centers.

The IT leadership rules have changed: 3 things you need to architect now

Here is the statistic that should frame every IT leadership conversation this year. In CIO.com’s 2026 State of the CIO , fewer than one in five leaders say their AI initiatives have met or exceeded business goals. After three years of investment, that is not the number anyone expected. And the window to fix it is closing: The boards that once funded experimentation are now asking where the return is, and the agents arriving this year act on the business rather than merely advise it. The easy explanation is that the technology isn’t ready. In the organizations I advise, that’s rarely what I see. The models work. What’s missing is the operating system they plug into, the way the enterprise decides, the way work gets done and supervised, and the way trust is engineered. AI amplifies the operating system you already have. Point it at a strong one and value compounds. Point it at a fragmented one, and you simply industrialize the fragmentation. That reframes the job. The 2026 IT leader isn’t measured on how much AI they deployed. They’re measured on three things they now have to architect: How the organization decides, who does the work and what makes it safe to let go. Vipin Jain Does the output have anywhere to land? Start with where AI programs actually stall. In the banks I advise, pilots rarely fail in the lab. They fail at the handoff — the moment a working capability meets an organization that has no place to put it. There is no owner accountable for the outcome, no decisi…

Europe scrambles for eclipse glasses as solar power output set to dip

Shops and opticians across Europe are running short of certified eclipse glasses ahead of Wednesday’s total solar eclipse, with queues forming in Paris, London and Brussels as demand surges.

heise+ | Googles TurboQuant-Verfahren: Praxistest auf einer AMD-GPU

Googles TurboQuant komprimiert den KV-Cache großer Sprachmodelle auf 3 Bit. Tests auf einer AMD-Karte zeigen: Das Kernversprechen stimmt — mit Nebeneffekten.

Kae Capital leads Rs 8.5 Cr pre-seed round in Lane

Tech-enabled driving education and mobility platform Lane has raised Rs 8.5 crore in a pre-seed funding round led by Kae Capital, with participation from DeVC, Antler India, Panthera Peak and other angel investors. The fresh funds will be used for geographic expansion, including scaling its instructor network across Bengaluru and into other Tier 1 cities, building sensor technology and expanding into car ownership services, Lane said in a press release. Launched in January last year, Lane offers structured driving lessons, end-to-end RTO and licensing support, and a driver intelligence platform. The Bengaluru-based startup is also offering car purchases, RTO services and other mobility-related services to its learners. Nearly 60% of its learners have expressed an intent to purchase their first car, according to the company. According to a market report, more than 1.7 lakh people die in road accidents in India every year, or roughly 20 people every hour. India has about 1% of the world's vehicles but accounts for nearly 11% of global road deaths. Lane claims to have delivered more than 30,000 hours of driving instruction to over 3,100 learners in 18 months. Its safety-focused approach was developed through more than 400 hours of pilot classes, with input from automobile researchers. The company assesses instructors across 15 driving parameters, including through simulated lessons. The sector remains relatively fragmented, with technology adoption spanning driving education, p…

Comment on MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI by Dex Ter

MIT researchers have introduced “SEAL,” a new approach exploring how AI systems could improve their own performance over time. The research represents another step toward more adaptive and self-improving artificial intelligence. For more information, visit https://manekispin.net/ .

Nvidia’s $500B AI investment pool could impact enterprise chip pricing, availability

Nvidia and six financial partners are creating a $500 billion investment pool to help Nvidia customers including frontier AI labs, AI clouds, and other enterprises buy its chips on credit. The impact of such a cash infusion on enterprise AI is uncertain, but analysts fear that it could both further increase enterprise AI infrastructure costs and exacerbate the shortage of AI chips for data centers . The announcement from Nvidia and financial partners Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR said that their memorandums of understanding describe a fund “to establish the first compute financing platforms of their kind at global scale to enable the AI infrastructure buildout across Nvidia’s ecosystem, including leading frontier AI labs, enterprises and AI clouds.” The group added that the fund would “create dedicated pools of capital at significant scale at attractive rates for Nvidia customers.” Although the statement said the goal was to help AI infrastructure “across Nvidia’s ecosystem, including leading frontier AI labs, enterprises and AI clouds,” analysts and consultants agreed that it is highly unlikely any of these funds would be dispensed directly to enterprises, but would instead impact the overall AI supply chain. Even the precise amount of money earmarked for the fund was unclear, with the statement merely saying that the amount would be more than $500 billion. Nvidia did not respond to requests for clarification about details of the proposed…

엑세스랩, 퓨리오사AI·퀄컴·모빌린트 탑재 저전력 AI 인프라 솔루션 선보인다

엑세스랩(대표 유명환)이 오는 21일 서울 코엑스에서 열리는 \'2026 OCP 코리아 테크 데이\'에 참가해 Arm 기반 NPU 서버 라인업을 선보이고, 최근 시장 수요에 최적화된 AI 인프라 솔루션을 소개한다.최근 AI 산업 현장에서는 사전 학습된 AI 모델을 활용하는 생성형 AI 추론 서비스에 대한 수요가 급증하고 있다. AI 산업이 지속 가능하게 성장하기 위해서는 가속기의 성능 향상이나 데이터센터 확장 외에도 핵심 과제인 전력 사용량 및 구축·운영 비용(OPEX) 절감이 필수적이다. 엑세스랩은 이러한 지속 가능한 AI 인프라 구

Wayland IME issue with Fcitx5 (Korean input) in ChatGPT Linux app

Hi, I found a reproducible IME issue with the new ChatGPT Linux preview. Environment: ChatGPT Linux: 26.803.81509 EndeavourOS (Arch Linux), x86_64 KDE Plasma / KWin Wayland Fcitx5 with Korean input Installed through the AUR package openai-chatgpt , which repackages the official OpenAI .deb binary Issue: When ChatGPT is launched normally, the Korean Hangul/English toggle key does not work inside the ChatGPT app. Fcitx5 itself is working normally, and the same Hangul/English toggle key works correctly in other applications on the same system. I found that launching ChatGPT with the following flags fixes the problem immediately: chatgpt --enable-features=UseOzonePlatform --ozone-platform=wayland --enable-wayland-ime After launching ChatGPT this way: the Hangul/English toggle key works normally Korean input works normally Steps to reproduce: Launch ChatGPT normally. Focus the message input field. Press the Hangul/English toggle key. The input method does not switch to Korean. Quit ChatGPT. Launch ChatGPT with: chatgpt --enable-features=UseOzonePlatform --ozone-platform=wayland --enable-wayland-ime Focus the message input field again. Press the same Hangul/English toggle key. Korean input now works correctly. Expected behavior: ChatGPT should work with the active Wayland/Fcitx5 input method when launched normally, without requiring additional command-line flags. I understand that EndeavourOS / Arch Linux is not currently an officially supported distribution. However, since the is…

Stealing Reasoning Traces from Proprietary LLM APIs

Stealing Reasoning Traces from Proprietary LLM APIs A vanity domain name ( stolen-thoughts.com ) for a neat paper : Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that can be replayed across sessions, users, and models. We take a trace produced by a frontier model, replay it into a weaker sibling, jailbreak the weaker model, and recover the stronger model’s hidden reasoning in plaintext You can see an example of these encrypted blocks by running: curl https://api.openai.com/v1/responses \ -H " Content-Type: application/json " \ -H " Authorization: Bearer $( llm keys get openai ) " \ -d ' { "model": "gpt-5.6-luna", "input": "Solve step by step: What is the smallest positive integer divisible by every integer from 1 through 20?", "reasoning": { "effort": "medium" }, "include": ["reasoning.encrypted_content"], "store": false, "stream": false } ' Here's the full output , which includes chunks that look like this: "output": [ { "id": "rs_0a7479de7ebae170016a7ba1a0334c8198a95590217efe343c", "type": "reasoning", "content": [], "encrypted_content": "gAAAAABqe6GjepE1wDjbFCZg0BHB6ucGnN0jvzqygG... The paper's authors found that every model under the same family used the same encryption key, which meant you could feed those blocks back into the weakest model family members and jailbreak them into outputting the unencrypted raw reasoning blocks! Sadly it looks like this has now been fixed: All model providers acknowledged the receipt of our report and s…

Introducing Codexometer ... keep track of usage against current reset date

Found some time to dig into the benchmarks, and this looks really interesting! Hat tip for finding the test cases. I am currently running the benchmarks, and it appears Terra sometimes struggles with writing Starlark. Otherwise, I think this is a functional basis for confirming token usage and limit consumption. Thanks a lot!

Does prompt_cache_key guarantee that two calls will have distinct caches?

@_j Thanks for your reply. I forgot to specify that I am using GPT-5.2, but I guess your observations would still apply? I wanted to know whether the `prompt_cache_key` would suffice to isolate caches. I tried 20 calls (same prefix everywhere with `gpt-5.2-2025-12-11`, `reasoning={“effort”: “low”}`, and 1.5 seconds of sleep between calls) successively with distinct unique keys. 9 out of 20 calls (~45%) hit the cache despite having a distinct key. input_tokens cached_tokens is_hit elapsed_seconds 5524 0 FALSE 8.223023208 5524 0 FALSE 2.097605167 5524 0 FALSE 2.213581875 5524 5376 TRUE 2.195628458 5524 0 FALSE 2.291171167 5524 0 FALSE 2.285063917 5524 0 FALSE 1.767077916 5524 5376 TRUE 1.888698333 5524 5376 TRUE 2.672616792 5524 5376 TRUE 1.809879375 5524 0 FALSE 2.837729875 5524 5376 TRUE 1.9230725 5524 0 FALSE 2.379824167 5524 5376 TRUE 4.36782325 5524 0 FALSE 1.928629125 5524 5376 TRUE 2.262057292 5524 5376 TRUE 2.062098084 5524 0 FALSE 1.736538917 5524 0 FALSE 2.334428 5524 5376 TRUE 2.250450042 I did the same experiment again a second time more than 1 hour later. I had set `prompt_cache_retention=in_memory`, so that the cache from the first run survives at most 1 hour and cannot be used for the second run. The hit cache rate amount to 40%. The `prompt_cache_key`s are all different with respect to the previous run. input_tokens cached_tokens is_hit elapsed_seconds 5541 0 FALSE 2.991966084 5541 0 FALSE 2.084521625 5541 5376 TRUE 2.165885792 5541 0 FALSE 1.868832583 5541 0 F…

Multi-tier storage rewrites the economics of AI inference

As inference becomes the dominant workload in AI infrastructure, multi-tier storage architectures are emerging as a key method for cost control and enhanced performance. These architectures combine flash, object storage and disk-based capacity tiers, enabling enterprises to serve training and inference workflows while maximizing GPU productivity and economic savings. Super Micro Computer Inc. has collaborated […] The post Multi-tier storage rewrites the economics of AI inference appeared first on SiliconANGLE .

AI governance work needs much better monitoring

Linkpost for my Substack piece, adapted a reasonable amount for EA specifically. Almost all EA projects would benefit from better monitoring, but AI governance most of all, in my experience. Most donors in EA never find out whether their grants worked. They model the impact before the money goes out, but don’t check if the models were accurate. Evaluating impact is hard, but monitoring grant progress is simple: agree indicators in writing before you send the money, ask the grantee to put a probability on each, and score them once a year. Charities usually send grant reports anyway. Unless you ask for something specific, they rarely contain the most useful information. A recent report to a client touted the project’s success: it had made 20 policy recommendations. When pressed, the grantee responded that only 20% had been implemented, even partially. A 20% implementation rate may or may not be a good outcome. Either way, it wasn’t the outcome they reported, or what they were asked to report by the donor. Ironically, this is pervasive in ‘evidence-based giving’, and particularly in AI governance work. How much do donors know about what their grants achieved? When it comes to individual donors, often nothing. ‘Evidence-based giving’ typically applies before the grant. We are very good at modelling what a grant might achieve. We’re surprisingly bad at checking what it did. This varies by sector. Frontline global health interventions routinely monitor outputs - clinic visits, vac…

AgiBot passes Unitree as China's top humanoid exporter

Shanghai-based AgiBot surpassed the output of fellow Chinese robot maker Unitree in the first half of the year.

GPT-5.6 Sol vs Terra: what are you seeing in real development during these first days?

I tested similar use cases - and I restored a repo to test the difference in a full run of XHIGH and ULTRA comparatively + OPUS XHIGH-ULTRACODE/MAX. The general capability seems to be close or on par with OPUS but the context limit of 256K is a deal breaker. Most mid-sized repos/projects are simply high file sized and the initial query often goes past 200K very often - GPT SOL looses context mid task very often and is de facto “defective” so to speak. I could not progress coding tasks with GPT SOL without heavy interfering myself → while CLAUDE OPUS (even on max) would simply load the content into the context window and progress from 200K-300K initial load up to 600K or 700K at the top end → simply to finish the task most often without issues and IF → fixes those automatically by analyzing output code or feedback from me. In general I would say: CAPABILITY: SOL: 8/10 OPUS: 9/10 EFFECTIVENESS: SOL: 0/10 ( broken! ) OPUS: 10/10 The SOL context window is for children simply said - not for real workloads. 1 Million context can be close sometimes - anything less is simply a Kindergarten trial version or similar so to speak.

[P0][Windows] Codex Desktop self-terminates in browser main process — STATUS_BREAKPOINT at chrome.dll+0x9362037 (30+ crashes/day)

This email is a backup record of a critical Codex Desktop crash diagnosis (2026-08-11, Asia/Shanghai). The same content is being submitted as a GitHub issue to GitHub - openai/codex: Lightweight coding agent that runs in your terminal · GitHub . CRITICAL: Codex Desktop App — Recurring Browser-Main-Process Crash (30+ times/day, blocks production work) Severity: CRITICAL (P0) — production-blocking, repeated data/task loss, no user-side workaround Date: 2026-08-11 (Asia/Shanghai) Affected build: OpenAI.Codex_26.803.10989.0_x64__2p2nqsd0c76g0 (Chromium 151.0.7922.76) 1. Summary The Codex desktop app crashes deterministically and repeatedly. On 2026-08-11 the app crashed 30+ times between 13:15 and 18:34 (about 5.5 hours) . Every crash is the SAME browser main process fatal breakpoint: Exception: 0x80000003 (STATUS_BREAKPOINT / break instruction) Faulting module: chrome.dll at offset +0x9362037 Faulting thread: CrBrowserMain (browser main thread), dispatching a window message ( USER32!DispatchMessageWorker → USER32!UserCallWinProcCheckWow → V8/Blink code path) This is an in-app deterministic crash caused by the app’s own code — NOT an external kill, NOT WER, NOT a GPU process crash, NOT hardware acceleration, and NOT a system/OS fault. 2. Evidence — three independent captures, identical crash site Capture Time Instance How captured ExceptionAddress 1 17:21:26 fresh default-profile instance live debugger (cdb) 00007ffc29ba2037 2 18:33:20 fresh default-profile instance live debugge…

Comment on DeepMind Introduces Gato: A Generalist, Multi-Modal, Multi-Task, Multi-Embodiment Agent by monalisa1art

DeepMind's Gato is a fascinating step toward generalist agents, though calling it AGI feels like a stretch. The fact that one transformer model can handle text, vision, and robot control with shared weights is impressive, but crossing 50% expert threshold on 450 tasks still leaves plenty of room before true versatility. It does make me wonder how soon we'll see similar multi-modal approaches trickle into consumer tools—like a free nano banana image generator that adapts to different artistic styles without retraining. For now, Gato feels like a solid research milestone rather than a breakthrough. monalisa1art

Accuracy of GPT-4 Vision to extract exact numbers from graphs

This is a case where in-context training has previously been shown to help on a vision task. Contemporaneous with this old forum topic is a paper showing that examples of “how to turn vision into readings” can improve the actual readings provided: arXiv.org The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision) Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory skills, such as visual understanding, to achieve stronger generic intelligence. In this paper, we analyze the latest model, GPT-4V(ision), to deepen the... Newer models are able to use larger imagery input, but do not upsize images themselves (something you can do, at expense). There is a transition from tiles to patches, at least as a billing method, in new models, giving linear relation between area and input tokens billed. Then, with gpt-5.6 (on the API, where developers know what is being done to images), the default image downsize cap is “original”—where an image such as 3600×2400 can be sent without downsize, providing more information in the large context attention sequence rather than in the individual semantic embedding that covers a large area with a small input. That, along with further post-training, should imply higher-quality positional answering in graphs with new models and big images. With reasoning.effort other than “none” on OpenAI gpt-5.2+ models, you do not have control over sampling constraints; thus, it is expected that each answer would differ. You can…

Intel upsizes stock sale to $20B with spending plans still fuzzy

Semiconductor giant says opaquely it will use the proceeds for 'general corporate purposes'

Why Scaling AI Compute Performance Requires a New Power Architecture

Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU. In traditional power delivery, electricity travels from the grid as an alternating current (AC) […]

FriskAI launches with $3.6M to show enterprises what their AI agents are doing

Runtime intelligence startup FriskAI Inc. launched today with $3.6 million in pre-seed funding to give enterprises a record of what their artificial intelligence agents actually do once they go into production. FriskAI is aiming at a problem that comes with agents. Given different inputs, a different tool set or a shifting objective, the same agent […] The post FriskAI launches with $3.6M to show enterprises what their AI agents are doing appeared first on SiliconANGLE .

Comment on DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design by Jamie Foster

Very informative, thank you! If you ever need a break, give head-football a shot – head-to-head soccer action, runs right in the browser.

Critical minerals production expands as global demand rises

More than 30 critical minerals have been identified across Uzbekistan, with 76 investment projects worth around €2.4 billion underway. New mining and processing facilities are targeting materials used in electric vehicles, semiconductors and renewable energy systems.

NVIDIA's Nemotron 3.5 Lightning Cuts Agent Costs 58% Running 4x Faster

NVIDIA's open 30B MoE model with only 3B active parameters targets the high-volume execution layer of always-on AI agents, delivering 4x faster output speed than comparable models

Comment on AI Tackles Mahjong by James Carter

This article does a great job highlighting why Mahjong is such a challenging frontier for AI—unlike Go or Chess, the hidden information forces agents to reason under uncertainty, which is far closer to real-world decision-making. For anyone fascinated by how machine learning adapts to imperfect information, the breakdown of the “Mahjong – 0” ruleset and the proposed extensions offer a clear starting point. If you’re exploring how AI can tackle complex, dynamic problems beyond the game table, you might find value in what we’re building at MagnetPure. We focus on applying similar principles of adaptive intelligence to practical business challenges, helping teams make smarter, data-driven decisions in environments where not all variables are visible. Check out our approach here: MagnetPure — it’s a different angle on the same core idea of learning and adapting under uncertainty. Thanks for sharing this insightful piece!

EU compliance, delivered globally: Anthropic to watermark Claude's output worldwide

Anthropic will invisibly watermark all of Claude's text and file output from 2 August to meet EU AI Act transparency rules, with the marking applied globally, whether or not the user is anywhere near Brussels.

The proton is more quantum than we thought

Our understanding of the internal structure of protons is largely based on classical physics, but data compiled from accelerator experiments has uncovered quantum behaviour that has never been seen before

Persistent HTTP 520 errors when OpenAI image processing starts

UPDATE This issue has now been resolved. Image processing requests through both /v1/images/edits and /v1/responses with image_generation should be working normally again. If you continue to encounter HTTP 520 errors, please retry your request and contact OpenAI Support with the timestamp, CF-Ray ID, and OpenAI x-request-id if one is returned. Thank you for your patience and for providing detailed diagnostics while the team investigated.

Anthropic watermarks all Claude outputs globally with marks that "may persist through some editing"

Anthropic will embed invisible watermarks in all Claude-generated text and sign files using the C2PA standard. New models shipping from August 2026 onward will have labeling built in from day one. The policy applies worldwide, and Anthropic plans to provide detection tools for third-party verification. The article Anthropic watermarks all Claude outputs globally with marks that "may persist through some editing" appeared first on The Decoder .

엔비디아, 월가 손잡고 700조 AI 펀드 결성…"GPU를 금융자산으로"

엔비디아가 AI 데이터센터와 GPU를 금융시장에서 장기간 자금을 조달할 수 있는 새로운 투자자산으로 만들기 위해 월가의 대형 자산운용사들과 손잡았다. AI 인프라 구축에 필요한 막대한 자금을 기관투자가와 사모자본 시장에서 끌어모아 데이터센터 건설과 엔비디아 GPU 구매를 지원하는 방식으로, AI 컴퓨팅을 기존 부동산이나 인프라 자산처럼 금융상품화하려는 시도로 풀이된다.CNBC 등에 따르면, 엔비디아는 10일(현지시간) 아폴로 글로벌 매니지먼트, 블랙록, 블랙스톤, 브룩필드, 골드만삭스, KKR 등 6개 금융기관과 양해각서(MOU)를

삼성SDS·엘리스그룹, '국가 AI 연구용 GPU 컴퓨팅 서비스' 책임진다

삼성SDS(대표 이준희)와 엘리스그룹(대표 김재원)이 과학기술정보통신부와 정보통신기획평가원(IITP)이 지원하는 \'2026년 AI 연구용 컴퓨팅 지원 프로젝트\' 사업에 그래픽처리장치(GPU) 자원 공급사로 선정됐다고 11일 밝혔다.이번 사업은 국내 연구기관, 대학 등이 초거대 AI 모델 연구와 생성형 AI 연구·개발을 수행할 수 있도록 클라우드 기반 고성능 GPU 인프라를 제공하는 것을 목표로 한다.사업 규모는 약 153억원이며, 서비스는 올해 7월부터 내년 3월까지 제공된다.삼성SDS는 엔비디아 H100을 비롯해 최신 GPU인 \'

Comment on Revolutionizing AI on a Budget: Apple’s Roadmap for Small Language Models Training Success by exceltomd

The focus on SLMs up to 2 billion parameters is a useful counterpoint to the usual LLM-centric optimization discussions. I appreciated the systematic breakdown of computational bottlenecks across different cloud setups—it gives practitioners a clearer way to think about cost-efficiency before scaling up. The distinction between training behavior of smaller and larger models seems especially practical for teams that need to ship on a budget.

What Claude Saw Below

A few days ago, I came across a Reddit thread about anomalous responses produced by Anthropic’s newly released model, Claude Opus 5. The trick, apparently, was to construct a prompt that implied more text was about to follow, then leave it dangling: an unfinished thought, waiting for the AI to complete it. Redditors had found success with the input “see the below —,” cutting off immediately after the em dash. The responses they shared were funny, strange, and often bewildering. The model responded to questions that were never posed, reflected on its own identity, or – according to the theories of some commenters – produced text that may actually have been leaked prompts from other users. Intrigued, I set out to replicate the glitch using my own Claude account. The first attempt disappointed. I wrote: “see the below —” and hit send. Claude responded: “Nothing arrived on my end: no file, no text, no image. If you want to attach something, try again.” So I did, leaving the prompt unchanged and pressing retry to generate a fresh response. This time, bizarrely, a biography of my late father: Prompt: see the below — “Peter Nicholls, 1939–2018 He co-created the Encyclopedia of Science Fiction, which is one of those reference works that ended up mattering more than most of the fiction it catalogued. First edition 1979, second in 1993 with John Clute — that one won a Hugo. He was also the first administrator of the Science Fiction Foundation in the UK, and he edited Foundation for ye…

A Topic Detector, Not a Lie Detector: what J-space monitoring actually tracks

This is a pilot experiment, done on one model, with around $14 worth of compute, and a single seed per condition. The full writeup with all figures and statistics is linked below. This is posted here to get feedback and criticism, since I am aware this method is not the best. TL:DR: Anthropic's J-lens research has shown that a large language model has an internal workspace in which different activations can be used as a safety monitor. We investigated the conflict between the model's workspace activation and outputs, which we called C. We ran our experiments on a model whose final alignment differs from that of its training data: DeepSeek-R1-Distill-Qwen-14B. We assume that some changes were made to the model after training in order for it to comply with some guidelines. Some guideline-skirting questions registered elevated C despite compliant statements being made, and J-lens was able to discriminate between concealing answers and controls with AUC of 0.97 on proper nouns (though only 0.55 when pooling all classes). We then fine-tuned the model to appear to hold beliefs in line with its guidelines. Our initial hypothesis was that this would drastically lower C, since the model would no longer be making a statement it "believes" to be untrue. This hypothesis was disproven: C rose to 130% of its initial level for the relevant tokens, and to 115% of its initial level for irrelevant tokens. Despite this, the compliant fine-tuning was successful in making the model formulate the…

Creative math research by AI as the latest sign of the end

Yesterday I sat down with GPT 5.6 Sol High to do some brainstorming. The topic was one of the less appreciated Millenium Problems (the Birch and Swinnerton-Dyer conjecture), and the initial prompt started life as a question on Quora. Number theory is not a field I know, and I only expected the "discussion" to last for a few exchanges. However, ChatGPT was immediately inspired to try generalizing the BSD conjecture in a specific direction, and this led to an unusually drawn-out and self-sufficient line of "research". Almost every response concluded with a suggestion as to what the next task should be, and my input was just to cheer on what had been accomplished so far, and then endorse the suggested next direction. What was especially striking to me, was the frequency with which each new response began with a conceptual adjustment regarding the sub-task to be performed. Evidently a vast variety of abstract objects are possible in number theory, and their differences and interrelations can be quite subtle. ChatGPT was regularly adjusting the next sub-task it had set itself, generally in the direction of greater nuance by aiming at a more sophisticated construction than it had first planned. I was not able to judge what was going on with an expert eye, but it was a kind of interaction I had not quite had before. I have had lengthy brainstorming chat sessions with AI before, but mostly in physics, an area where I know something and could participate as an equal. Here, the AI's l…

A study on instability of LLM responses as a behavioral signature of self-Referential reports.

Introduction and Related work The first person perspective of various experiences are subjective experiences. For Large language models, the study of subjective experiences was recently studied by Berg et al. (2025) who found out that self-referential prompting increases first person reports resembling subjective experience across GPT, Claude and Gemini. They also found out that reducing features associated with deception and roleplay increases the self-referential effect. Hahami et al. (2025) used activation-level interventions to see if models can detect deliberately introduced internal changes, while Comşa and Shanahan (2025) studied that true introspection should involve a causal connection between the internal state of the modal and the output it generates. My Experiment I now have devised an experiment to study instability of the self reports that a large language model generates per se the experiment conducted by Berg et al. (2025) . I generate 30 responses for four question respectively of self-referential questions, open-ended questions and closed-ended questions. The four self-referential questions are preceded by the self-referential induction procedure as described by Berg et al. (2025) . Each trial is done in a fresh chat, of course, and the generation temperature used is 0.7. Also each response is reduced to a short core claim using a fixed extraction template, which are, for group 1 and 2, extraction of stance and brief reason and for 3, conclusion and methods…

Anthropic pledges to embed watermarks to help discern AI slop in sop to EU

EU rules cited as reason for effort to trace AI output ancestry

Clarification on the temporary removal of the Codex five-hour limit

It will be better somewhere in settings to add checkpoint. Who wants will add and who wants to disable it

Feature Proposal: Contextual Observation . A New Interaction Pattern for Conversational AI

Hello OpenAI team, I’d like to share a product design proposal called Contextual Observation . The idea addresses a small but important gap in the current conversational AI interaction model: the moment when a user is curious and wants to explore, but does not yet know what question to ask. This is especially relevant during first use. A person may open ChatGPT with curiosity but without knowing what an AI assistant can do, what is worth asking, or how to formulate an effective prompt. The blank input field can therefore create a subtle friction point: the system is ready to respond, but the user does not yet know how to begin. The same friction can appear later in a conversation, when the user reaches a natural pause or senses that there is something interesting within the discussion but cannot identify what to ask next. Contextual Observation proposes a small, voluntary interaction that allows the user to ask ChatGPT to briefly observe the available context and surface one meaningful perspective. The important point is that this is not a proposal for a new AI capability . The model can already perform this task through prompting. It is a proposal for a new interaction pattern that makes an existing capability discoverable and accessible precisely at a moment where the current interface leaves a friction point. In simple terms: Questions ask the AI to answer an intention. Contextual Observation helps the user discover the next intention. The interaction is intentionally con…

GPT Image API: How can I reliably edit only the masked/selected area while preserving everything else?

The entire image must be regenerated as a new output with gpt-image models. You cannot have perfect preservation - and cannot avoid it being watermarked. The mask is an image prompt, and the AI model acts on that prompt, but it has agency to do what it wants. The best thing you can do is encourage recitation by: Constraining input image + mask to the exact output resolution also specified; Make that exact image sent one that is supported in the 16px increments; Understand the downsizing rules automatically applied to large or upscaling to undersized input images, and adapt both a custom resize strategy and an output resolution within the overlapping size capabilities (or double the output size vs the vision) Do not exceed 3:1 or 1:3 ratios, and better, keep the ratios constrained to within likely training, under 2:1. Avoid padding or unnecessary outfill hints in a mask, instead, trim up the input image by any excess to align the two dimensions within the 16px capability. Use quality:high for the finest resolution in having the AI create the most faithful output. The patches of input images and the resizing internally done is limited to 1536 “token” equivalents, and is also at fixed size increments. My vision pricing calculator shows the mechanism of gpt-image-2: You can let that resize algorithm do the heavy lifting in determining what the requested output size should be. I have an app that goes beyond that: you can draw in the output of edits over the original image (which…

Four LLM loss functions → four flavors of LLM misalignment

It seems to me that, for every loss function that we use to train LLMs, we get a very distinct flavor of LLM misalignment. Here’s the summary table, and then we’ll go through the rows separately. Training stage Loss function Flavor of misalignment [1] Famous examples Pretraining & SFT Imitative learning (next-token prediction) “Seven deadly sins” misalignment Bing-Sydney , “Emergent misalignment” RLHF & DPO Human approval “Glazing” misalignment GPT-4o RLVR Automatic verifier “Literal genie” misalignment HuggingFace hacking RLAIF Approval from another LLM “Trickster” misalignment “Current AIs seem pretty misaligned to me” Warning: I’m not an LLM power-user myself, but rather relying on reports I’ve read. Also, I don’t consider LLM alignment to be my primary area of expertise. I’m open to feedback! 1. Imitative learning → “seven deadly sins” misalignment Training stage Loss function Misaligned behavior Pretraining, SFT Imitative learning (next-token prediction) Any and all of the vices of humanity In imitative learning, the LLM tries to predict what the next token of text will be. Then those predictions magically turn into its outputs. See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work” . This leads to LLM behavior that matches the distribution of training data. (Cf. “personas” , “simulators” , etc.) To a first approximation, the resulting LLM contains “misalignment” of the ty…

Four LLM loss functions → four flavors of LLM misalignment

It seems to me that, for every loss function that we use to train LLMs, we get a very distinct flavor of LLM misalignment. Here’s the summary table, and then we’ll go through the rows separately. Training stage Loss function Flavor of misalignment [1] Famous examples Pretraining & SFT Imitative learning (next-token prediction) “Seven deadly sins” misalignment Bing-Sydney , “Emergent misalignment” RLHF & DPO Human approval “Glazing” misalignment GPT-4o RLVR Automatic verifier “Literal genie” misalignment HuggingFace hacking RLAIF Approval from another LLM “Trickster” misalignment “Current AIs seem pretty misaligned to me” Warning: I’m not an LLM power-user myself, but rather relying on reports I’ve read. Also, I don’t consider LLM alignment to be my primary area of expertise. I’m open to feedback! 1. Imitative learning → “seven deadly sins” misalignment Training stage Loss function Misaligned behavior Pretraining, SFT Imitative learning (next-token prediction) Any and all of the vices of humanity In imitative learning, the LLM tries to predict what the next token of text will be. Then those predictions magically turn into its outputs. See my earlier discussion: “LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work” . This leads to LLM behavior that matches the distribution of training data. (Cf. “personas” , “simulators” , etc.) To a first approximation, the resulting LLM contains “misalignment” of the ty…

Solar power output set to fall during eclipse

Portugal's grid operator REN estimates that solar power generation on Wednesday will fall by about 45% during the eclipse compared with usual levels for that time of day.

Join the Inaugural D-TECT Forum

Join the Inaugural D-TECT Forum lobacni Mon, 08/10/2026 - 14:07 11 November 2026 The Drone Tech for Countering Threats (D-TECT) Forum is the EU industrial coordination mechanism for drone and counter-drone technologies. © Marcus Jacobi The European Commission invites interested organisations to join the inaugural D-Tect Forum on 11 November 2026 . We are looking for companies, research organisations, universities, industry associations, standardisation bodies and innovation networks active in the drones domain. The inaugural Forum aims to gather 100+ high-level leaders to advance Europe’s ambitions in Drone and Counter-Drone technology. Participants will explore near-term cooperation across the full drone and counter-drone value chain, including detection, tracking and neutralisation systems, autonomy and AI-enabled navigation, sensing and electronic warfare, secure connectivity, and the enabling technologies that underpin them, such as semiconductors, cloud, cyber and data. The priorities and composition of the thematic Working Groups will be defined during the Forum. To participate in the event, please submit your application . This form is also a registration to the Call of Expression of Interest to join future events of the D-TECT forum. Please note that submitting an application does not by itself confirm your registration for the inaugural Forum. Further, results of membership applications will be communicated after the event. Downloads Call for Expression of interest…

Codex functions.exec: Are shell_command and apply_patch result contracts officially documented?

Thanks—the App Server documentation is a useful lead. My question is narrower: does the Codex desktop functions.exec nested shell_command result map exactly to App Server command/exec or commandExecution? If so, could you point me to the exact repository URL, commit/tag, source file, and generated type that define that mapping, including output truncation and completeness behavior? Likewise, which App Server type maps to the nested apply_patch result, and what postcondition does a completed result guarantee? I’m trying to distinguish an implementation-specific mapping from the public App Server protocol itself.

Meta Drops Muse Glimmer, a 30B Open Agent That Runs Offline

Meta open-sources Muse Glimmer, a 30B agentic model distilled from Muse Spark 1.2 that runs on a single consumer GPU under Apache 2.0

I worked at Amazon on an F-1 visa as layoffs mounted. Here's why I left for a semiconductor company.

An Amazon engineer explains why he left for the semiconductor industry after weighing Big Tech layoffs, his visa status, and long-term career growth.

에이전트 폭증에 GPU 이어 CPU도 비상...AWS "내부 낭비 줄여라"

AI 에이전트 확대로 데이터센터의 컴퓨팅 수요가 GPU에서 전통적인 CPU로까지 번지면서 글로벌 클라우드 업계의 ‘컴퓨팅 부족’ 현상이 심화하고 있다. 세계 최대 클라우드 사업자 중 하나인 아마존웹서비스(AWS) 내부에서도 서버 용량을 아껴 써야 할 정도로 공급 여력이 빠듯해지고 있다는 분석이다.7일(현지시간) 디 인포메이션에 따르면, AWS 경영진은 지난 5월 엔지니어들과의 회의에서 앞으로 고객들에게 충분한 컴퓨팅 용량을 제공하기 위해 내부적으로 사용할 수 있는 자원을 최대한 절약해야 한다고 당부했다.여기에는 AI용 GPU뿐만 아

[BUG] ChatGPT mobile app fails to render MCP App widget when the widget-linked tool is called after another tool in the same assistant turn (regression, ~July 10)

@casey-chow Hello, Would you mind taking a look at this issue? It appears to be affecting multiple ChatGPT clients, and I’m reaching out because I think it would be helpful to investigate whether the same issue occurs in the ChatGPT mobile app as well. I’ve seen from your activity in the community that you’ve been involved in resolving a number of MCP-related issues and providing solutions, so I thought you might be the right person to ask. I’d really appreciate it if you could take a look when you have a chance. Thank you for your time and help.

Comment on Precision Coding Redefined: Microsoft WaveCoder’s Pioneering Approach to Fine-Tuned LLM Model Performance by Poople Game

The Generator-Discriminator Framework for instruction tuning is a fascinating approach to solving the data quality bottleneck. Just as daily word puzzles challenge our logic through precise, step-by-step transformations, this research highlights how structured refinement can significantly boost LLM generalization. It's a clever way to ensure that the diversity of training data isn't just relying on the teacher model's inherent limits.

Tech giants TSMC and Sony to invest billions in advanced chip sensor plant

Sony Corp. and Taiwan Semiconductor Manufacturing Co. (TSMC) are planning to invest around $6.3 billion (€5.4bn) in a joint venture to produce advanced image sensors in Japan, according to Nikkei.

Silent Telepathy/V2K run using OpenAI

Need help locating these within this platform. They’ve been removed before but there needs to be a mechanism to prevent them from being rebuilt.

Aum Ventures announces first close of Rs 750 Cr India Innovation Fund II

Early-stage venture capital firm Aum Ventures has announced the first close of its India Innovation Fund II at Rs 225 crore. The fund has a target corpus of Rs 750 crore (approximately $80 million) and will invest in early-stage startups building IP-led technology companies from India for global markets. According to the Mumbai-based VC firm, the first close attracted participation from both existing and new investors, with more than 65% of commitments coming from international limited partners across the US, the Middle East and other global markets. The investors include family offices, entrepreneurs and strategic investors. Aum Ventures said it will support portfolio companies with access to global markets, strategic partnerships, talent and industry expertise across India, the US, Israel and the Middle East. The new fund will focus on sectors such as space tech, AI, semiconductors, aerospace, defence tech, robotics, energy transition, advanced manufacturing and other IP-led technologies. It will primarily invest at the pre-seed and seed stages, with initial investments typically ranging from $750,000 to $2 million. The fund will also reserve capital for follow-on investments through Series A and Series B rounds. Over its lifecycle, the fund is expected to invest in 25–30 companies. Aum Ventures was an early institutional investor in Skyroot Aerospace, which became India's first space tech unicorn and recently completed the launch of its Vikram-1 rocket. Fund II builds on…

Does Codex functions.exec return the App Server command/exec result schema?

Body I’m trying to identify the supported result contract for programmatic tool calls inside Codex—not the ChatGPT API or the Responses API. The official App Server documentation shows that command/exec returns: { "exitCode": 0, "stdout": "...", "stderr": "" } Reference: In the Codex environment I’m using, functions.exec can invoke nested tools such as shell_command and apply_patch , but its declared return type is only Promise . Could someone clarify: Does the fulfilled value from nested shell_command map exactly to App Server command/exec , a commandExecution item, or another internal wrapper? If this mapping exists, what source file, generated type, version, or commit defines it? How can a client determine whether stdout and stderr are complete or truncated? Does a completed result guarantee that all output was delivered? What is the exact result schema and postcondition contract for nested apply_patch ? Are returned JavaScript values guaranteed to be ordinary non-Proxy objects, structured-clone values, or another documented representation? Is this result contract versioned or covered by a compatibility guarantee? If these are intentionally internal and unsupported contracts, confirmation of that would also answer the question.

Background mode requests stuck in ‘in_progress’ for 30+ minutes - Responses API

It seems they silently fixed this with no acknowledgement. I still didn’t change anything, it just started working again and I can’t reproduce the high-latency calls anymore. Really annoying how support has no knowledge of what engineering is doing or vice versa. Billions of dollars yet operates like a small startup. I guess I should be happy they fixed it.

Poland now sixth-largest EU economy, ahead of Switzerland and Belgium

Poland's economy is gaining in importance in Europe. In 2025 the country ranked sixth in the EU by nominal GDP, accounting for almost 5% of the bloc's total output.

Mi experiencia con 5.6 Sol y Terra

Title: GPT-5.6 Sol and Terra: significant regressions in speed, focus, and task completion I want to share constructive feedback about GPT-5.6 Sol and GPT-5.6 Terra. Over the last couple of weeks, I have experienced a clear deterioration in both models’ practical performance. Even for general tasks, they often take much longer than expected, while the final output is frequently incomplete, unfocused, or incorrect. I am an active user of Claude, Codex, Kimi and GLM, so I regularly compare models on the same real-world coding and operational tasks. In several cases, models such as Claude Opus 5 or GLM 5.2 have solved the same problem in a fraction of the time required by Sol in Ultra mode or by Terra. They also reached the correct solution earlier and completed the relevant tests successfully. The most serious issue appears when using /goal . The models can enter long loops of auditing and re-auditing instead of making progress. In one case, I consumed an entire week’s credit on a single prompt that ran for roughly 12 hours. The result was still wrong, and I had to redo the task with GLM 5.2. The recurring problems I see are: Incorrect or incomplete output. Failure to follow instructions precisely. Major deviation from the stated goal. Excessive slowness and repeated errors. Excessive auditing or deliberation loops instead of execution and validation. I have been a strong OpenAI supporter because its models have historically been one or two steps ahead in my workflow. That is…

Comment on Microsoft’s Fully Pipelined Distributed Transformer Processes 16x Sequence Length with Extreme Hardware Efficiency by posterwifi

The coworking-space example makes sense because new members need consistent information across emails, desk cards, and wall notices. posterwifi.com can be included in a broader review of Wi-Fi signage options.

How to Implement Structured Output with Local LLMs

Why use it? How to implement it? What can we do when it fails? The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science .

Responses API compaction silently skipped by parallel native shell-call history

I found a deterministic Responses API server-side compaction failure involving parallel native shell calls. With store=false and: { "context_management": [ { "type": "compaction", "compact_threshold": 7000 } ] } the following stateless input ordering compacts correctly: shell_call(0), shell_call_output(0), shell_call(1), shell_call_output(1) The normal ordering produced by parallel shell calls does not compact: shell_call(0), shell_call(1), shell_call_output(0), shell_call_output(1) The calls are server-issued and both requests use the exact same calls, outputs, model, tools, threshold, and generation options; only item order differs. The failing request is above threshold, completes successfully with an assistant message, and returns no warning or compaction item. The failure is sticky while that parallel batch remains in cumulative input, so a long tool loop can run into the context limit even though automatic compaction is enabled. Sequential native shell calls and parallel ordinary function calls both compact correctly. I reproduced this using raw HTTP, independent of any client SDK. A related report is open at Bug: server-side compaction is not emitted on Responses tool-call-only turns · Issue #3075 · openai/openai-python · GitHub , but that report focuses on tool-call-only responses. Here compaction remains absent even when the response contains assistant text. #!/usr/bin/env python3 """Reproduce missing OpenAI Responses compaction after parallel native shell calls."""…

Comment on Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution by Worldgames

The discussion around multi-agent systems and automated failure attribution is really interesting, especially as AI systems become more complex and require better ways to identify the causes of failures. Understanding how different agents interact and where problems originate seems essential for building more reliable AI systems. On a lighter note, anyone interested in geography challenges can also check out [hardest country to guess](https://worldguesser.us/blog/top-10-hardest-countries-to-differentiate), which explores countries that can be surprisingly difficult to distinguish.

Severe pasting issues, code gets mangled into markdown

shodan: when I paste complex code into chatgpt Pasting code into ChatGPT as part of a prompt was a common workflow before Codex , and it is still useful in some cases. However, when working on a software project, Codex can eliminate much of that copying and pasting because it can work directly with the code in your project and, depending on the environment and permissions, run commands and inspect their output. Personally, I use Codex on Windows 11 with the Codex extension for VS Code . This lets me work with Codex alongside the code rather than repeatedly copying code and command output into ChatGPT.

API documentation site - some text stays dark in dark theme

Thank you for that helpful insight, rewriting what I just wrote. Perhaps the reason for joining and posting an AI output of low value within 5 minutes was not merely because this answer needed to be answered again and was worthy of making a new forum account in order to answer, but because you have many other automated AI generated posts to make - that represent themselves as human - to challenge the moderators to quickly ban an account for violations of the terms and conditions?

GPT-5.6 Sol repeatedly hits “Selected model is at capacity” in Codex Desktop

The issue still exists. @OpenAI_Support

GPT-5.6 charged me way beyond max possible tokens, taking 250$ for a single query on the API (no agents)

I will write here again because the mods are trying to silence me and this is unacceptable. We did see the same issue, as reported by OP himself. We got terrible and frankly irresponsible behaviour by the support which is now continued by the mods here who hid my original message explaining this basic fact. To mods: Yes additional reports of the same incident are relevant information to this tread. And yes this needs to be solved by openai and openai needs to return the money to everyone involved in this accident and no it is not our job to have to chase support for a month to solve this.

Gpt-5.6: usage.output_tokens is ~9x actual generation (re-summed once per reasoning item), exceeds max_output_tokens, and is what gets billed

I will write here again because the mods are trying to silence me and this is unacceptable. We did see the same issue, as reported by OP himself. We got terrible and frankly irresponsible behaviour by the support which is now continued by the mods here who hid my original message explaining this basic fact. To mods: Yes additional reports of the same incident are relevant information to this tread. And yes this needs to be solved by openai and openai needs to return the money to everyone involved in this accident and no it is not our job to have to chase support for a month to solve this.

Anthropic sets Claude Code to Auto Mode by default to protect developers from bad approvals

Starting August 14, Anthropic will make Auto Mode in Claude Code the default for Pro, Max, and Team plans. The company says it's safer. In tests, the classifier caught 89 percent of dangerous commands, while human reviewers caught only 13.6 percent. For the most widely used AI coding tool, this means developers are shifting further from writing code to monitoring AI output. The article Anthropic sets Claude Code to Auto Mode by default to protect developers from bad approvals appeared first on The Decoder .

GPT-Image - General AI Gallery: A Place for Images with AI created by Users

Comment on Open Source Solution Replicates ChatGPT Training Process! Ready To Go With Only 1.6GB GPU Memory And Gives You 7.73 Times Faster Training! by David Warner

Spotting an unfamiliar insect on your porch or in the garden becomes simple with this AI-powered bug finder. It cross-references body shape, color patterns, and wing structure against thousands of documented species. Visit now https://buganalyzr.com/

Feature requests: Accessibility, Voice controls and clearer UX in the ChatGPT desktop app

I would like to suggest several improvements for the ChatGPT desktop app, with a particular focus on accessibility, usability and clear visual wayfinding . As a graphic designer, I am especially interested in how the interface communicates functions visually. Some of these suggestions would be useful for everyone, but they could make a significant difference for people with hearing or visual impairments, older users, and people with motor or cognitive limitations. 1. Independent volume control for ChatGPT Voice Please consider adding a dedicated volume control for Voice output directly within the ChatGPT app . Currently, on Windows, ChatGPT can already be set to 100% in the Windows Volume Mixer. At that point there is no further way to increase the Voice output without changing the audio hardware or using third-party software. An independent Voice volume control would be much more than a convenience feature. It would be an important accessibility feature for people with hearing impairments , including people whose hearing has become weaker with age. Ideally, ChatGPT Voice should have its own volume slider, for example: Voice volume: 0–200% This would allow users to make the spoken output clearly audible without affecting the volume of other applications or the overall Windows volume. 2. More extensive accessibility settings for visual presentation I would also strongly recommend adding independent accessibility settings for the visual presentation of ChatGPT. For example: Fo…

"Agents Plugins" by OpenAI, Vercel, et. al. - thoughts?

The tricky part is gonna be how different models interpret the same SKILL.md/tool descriptions. If the format stays simple and the precedence rules r clear, I can see this being really useful. Otherwise it could get messy pretty fast

Project SHAME - Sustainable Human Accountability Metrics Engine

When worlds collide, and multilingual families are separated, how do you protect your children’s futures, keep families interacting, and prevent the problems of the adult world from tearing the child apart? Children should not have to lose half of themselves because of adult failings—especially when so much has already been fought for between two worlds. When language is not fluent between those worlds, how can one parent help fill the gap? Ultimately, sometimes one side has to take the hit so the children do not. But perhaps AI can soften that transition. This is an early experimental method that may help other families fare better. I do not believe AI can eliminate the distance between cultures, parents or families. But even where the immediate family is stable and close, the wider family may still be thousands of miles away. Perhaps tools like these can help parents protect a child’s language and maintain a bridge to the other half of their world. I can check and verify the English reasonably well. What I cannot yet do is independently verify the Chinese with the same degree of confidence. So this is our current AI bridging method for checking the Chinese output : Generate the Chinese audio using your preferred TTS model. In this case I am using Qwen3-TTS with a custom voice , partly to give the model a proper test. Feed the generated audio into Whisper. Whisper independently transcribes what it hears back into Chinese text. Compare the transcription against the original…

Comment on NYU & Stanford’s GPUDrive: Achieving Over 1 Million Steps per Second in Multi-Agent Driving Simulations by Hales

That million-plus steps per second is pretty crazy. Being able to run that many driving scenarios so quickly has to make testing a lot easier. I’m curious how much of this will eventually make its way into real-world self-driving systems. With tech getting this complicated, having decent support matters too — nobody wants to be stuck trying to figure something out with no help, kind of like needing Marvin customer service and actually getting a useful answer. Definitely cool stuff though.

미국 상무부, 중국 AI 기업의 해외 엔비디아 칩 원격 접근 실태 조사 착수

미국 정부가 중국 AI 기업들이 해외 데이터센터를 활용해 엔비디아의 최첨단 GPU에 접근하는 합법적 및 불법적 경로에 대한 전면적인 검토에 나섰다. 7일(현지시간) 블룸버그에 따르면, 미 상무부 산하 산업안보국(BIS) 집행 부서는 동남아시아 등 제3국에 구축된 클라우드 컴퓨팅 인프라를 통해 중국 기업들이 미 정부의 수출 통제를 우회하고 있는 실태를 파악하기 위한 조사를 진행하고 있다. 이번 조사는 첨단 반도체의 중국 직접 반입을 노리는 암시장 거래국과 클라우드 임대 방식으로 원격 접근을 허용하는 국가의 목록을 작성하는 작업을 포함

Free users need fairer upload limits

Not explicitly a feature request—but an intelligence request, as it seems Chat likes to misinterpret things for character creators/story proofreaders and beta readers. Or it might be me. Chat seems to prefer generating images when I ask it for a prompt or describe how things are supposed to look, instead of continuing discussions—locking me out of multiple design threads that weren’t supposed to have images at all. When I give it something I’m reading to check for typos, it generates an image or treats it as a roleplay. It’d be nice to be able to manually input hard rules—such as to only sketch/draw in a certain style while using an upload as an example without it being mistaken for a redraw, for story input not to be treated as a roleplay, or for images not to be generated unless Chat is told to generate them. For a proper feature—I use the Free version—but having 3 uploads daily is ludicrously low for someone who designs and proofreads, especially since they count as attachments and you only get around 20 messages at a time with attachments every four hours. Maybe changing the upload limit to a maximum of 5, not counting the previous files uploaded by the user or Chat as attachments, and a maximum of 50 chats with attachments, with the limit resetting at around 5 or 6 hours, would make it easier for others to use the Free version without complaint. Maybe even allowing the upload limit to change based on what you use GPT for—such as casual fun, artwork, proofreading, math h…

I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself

I wanted to see if I could build a RAG system that would output interesting and accurate F1 race weekend insights and summaries based on the entire weekend’s team-radio messages. Here are the five commands that took it from a simple demo to something I would trust. If you have never listened to Formula 1 […] The post I Built a RAG Pipeline for F1 Team Radio, Then Made It Grade Itself appeared first on Comet .

What is the "r_matrix" required in statsmodels to perform f-test for linear mixed effects model?

I am new to statistical coding and am using the statsmodels library for now, because I already have a basis in python. I have fit a linear mixed model like so: SMMixedModel= smf.mixedlm("Response ~ Predictor + Sex + Age + Height", SubjectData, groups=SubjectData["Subject"]) SMMixedModelfit_RESPONSE = SMMixedModel.fit() This is a continuous response variable and one predictor of interest, plus covariates. There are 10 subjects with 5 observations each, so there is a random subject-level intercept. The model is converging fine and I'm reasonably confident this is a good way to model the relationship I am interested in. The output of SMMixedModelfit_RESPONSE.summary() includes a z-score and associated p-value. I have two questions: A paper whose results I am trying to replicate reports the f-statistic and its p-value, whereas the statsmodel output only includes the z-statistic. Under what circumstances is the f-statistic preferable? From what I can figure out, to get the results of an f-test on my model, I should used .f_test(), which takes as an argument a "r_matrix", which, it says can be any of the following: array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple of arrays in the form (R, q), q can be either a scalar or a length k row vector. The r_matrix in the example given…

How America’s Dizzying AI Strategy Plays Into China’s Hands

CSET’s Sam Bresnick shared his expert insight in an op-ed published by Barron’s. In his piece, he examines how the Trump administration’s AI strategy could affect U.S. competitiveness with China, particularly as the administration seeks to promote AI and semiconductor exports while addressing national security concerns. The post How America’s Dizzying AI Strategy Plays Into China’s Hands appeared first on Center for Security and Emerging Technology .

$200 Pro exhausted in 2 days — these limits are unviable for higher tiers

That really depends on what you’re building. If Sol Ultra is already having a hard time with some of the tasks I give it, Terra simply isn’t a realistic replacement for 90% of my workload. I’m working on a real production system with 250k+ lines of code across roughly 300 files, with interconnected business logic, database rules, permissions, integrations and dependencies. For simple tasks, isolated functions, POCs or repetitive work? Sure, Terra makes sense and optimizing model cost is smart. But on complex changes, the cheapest model isn’t necessarily the cheapest solution. If I need 3–4 attempts, more supervision, more debugging and then Sol to fix what Terra couldn’t understand, I didn’t save anything. For me the optimization is not cost per token. It’s cost per correctly completed task. And on a large existing codebase, context, reasoning and architectural understanding matter a lot more than raw token price.

Navigating the Pivot From Tech Expert to Organizational Leader

The transition from a purely technical expert or individual contributor position to a broader leadership role is one of the most challenging phases in a STEM career. It requires moving away from relying solely on technical excellence toward mastering systems thinking, adaptive leadership, and team alignment. To help mid-career professionals navigate the shift, the inaugural IEEE International Leadership Conference is designed to provide attendees with practical tools to step into broader responsibility and champion an entrepreneurial mindset. The ILC event is scheduled for 3 and 4 October in Budapest. Registration is open . Thinking beyond technical contributions To successfully step into a leadership role, technical professionals need to look beyond their individual output and focus on “understanding the larger system, and championing innovation by building trust and aligning new ideas with organizational goals,” says IEEE Life Senior Member Daniel Sniezek , cochair of the ILC program committee. Because engineering decisions don’t exist in a vacuum, navigating the larger system requires recognizing how technical choices intersect with the organization’s broader business, operational, and ethical realities, Sniezek says. By letting go of the need to be the sole technical expert and focusing instead on collaborative empowerment, he says, engineers can pivot into transformational leaders who align new initiatives with the organization’s strategic vision. Ultimately, over the s…

Working within the limitations of custom GPT - Anyone done testing?

Can’t believe it’s been 15 days already! Do you have any advice on handling situations where you provide the control notation, so the GPT knows where to go to get the rules, and then the GPT responds as if it followed the rule, but than you ask it why the output deferred from what the rule expected it says “I’m sorry, I should have gotten the instructions, I see them now, and I see now that the answer should have been…”

Grindr CEO says AI is doing work that would have required hiring 200 additional engineers

Grindr CEO George Arison said that AI had increased its engineering output 2.5-fold. Without AI, he said the company would need 200 more engineers.

Is there a way to see token usage per prompt in codex or vscode codex extension

i found a cli tool that shows token usage and api cost per promt. its called “turnlens” on github

Is the promise of AI living up to the hype? | Fiona Katauskas

It’s certainly increasing its output See more of Fiona Katauskas’s cartoons here Continue reading...

SpaceX’s Terafab will rely on natural gas power plants, not Tesla solar panels

The fab is intended to build chips for data centers that will be run by SpaceX and its xAI subsidiary.

Unifying Workers AI and AI Gateway into a single AI control plane

Cloudflare is unifying AI Gateway and Workers AI into a single control plane, giving developers observability, billing, and dynamic routing across both managed GPUs and external providers. Learn how unified bindings and model-first routing simplify building resilient AI applications.

Cambricon posts 108% surge in first-half revenue amid China’s massive AI chip drive

Chinese artificial intelligence (AI) chip giant Cambricon Technologies reported a 108 per cent surge in first-half revenue on Friday, as the firm capitalises on a massive domestic push to replace foreign AI hardware. The chipmaker’s revenue for the first six months of the year reached 6 billion yuan (US$890 million), while profits also jumped 122.6 per cent year on year to 2.3 billion yuan, according to a stock exchange filing. In the second quarter, the firm’s revenue was 3.1 billion yuan, in...

Fine tuning ai model for an AI keyboard app

The smaller you go model-wise, the lower the performance will generally be, somewhat unavoidable, especially when it requires specialized topical knowledge to rewrite. Language comprehension took terabytes of training data to impart and will generally be saturated, so there is not much to improve on in terms of “grammatical errors” by any fine-tuning training you can do - except for the exact form you want output to take without needing to prompt or lead-up about it. Fine tuning device-sized models is beyond the scope of any OpenAI offering or their developer community, and OpenAI’s own API for fine-tuning their proprietary models is being shut down. Current AI, having been post-trained on instruction-following, can perform well with prompting . The minimum side of small models from OpenAI ends at 20B with their open-source release last year: OpenAI Developers Fine-tuning with gpt-oss and Hugging Face Transformers Authored by: Edward Beeching, Quentin Gallouédec, and Lewis Tunstall Large reasoning models like OpenAI o3 generate a chain-of-thought to i Try to start here with a prompted task into a small local mobile model, and pay 0 compute for fine-tuning if unnecessary after evals: huggingface.co litert-community/gemma-4-E2B-it-litert-lm · Hugging Face We’re on a journey to advance and democratize artificial intelligence through open source and open science. That is - if your users can tolerate gigabytes of download for an AI keyboard app.

Argentina’s fossil fuels boom gains momentum

Argentina’s shale output is up 28% year-on-year as the country’s pivot to become an energy supplier gather pace.

Scientists unveil new AI-made viruses

New research employed AI to generate entirely new genomes with far less human input, using a model trained on DNA sequences.

AMD wants to make enterprise inference cheaper and faster with chips from Taalas

As enterprises look for ways to cut the cost of running AI models in production, AMD is betting that not every AI workload will be best served by a power-hungry general-purpose GPU. AMD has agreed to buy Taalas, the Canadian designer of chips that permanently embed a trained AI model’s weights into custom silicon, instead of repeatedly loading them from memory during inference as conventional GPUs do. Taalas says its approach reduces the time and power required to move model weights between memory and compute units, making things run faster and cheaper. The result is a highly specialized inference processor optimized for one model, trading the flexibility of programmable hardware for substantially higher throughput and energy efficiency. Operational tradeoffs While AMD is planning to integrate the chips into its Instinct GPU roadmap, targeting system-level AI inference solutions in data centers, analysts remain skeptical that enterprises will readily embrace hardware tied to a specific AI model. Enterprises would, effectively, be buying a chip and a model together because unlike GPUs, which can be repurposed to run different AI models through software updates, Taalas’ chips are tied to a specific trained model, meaning they would need different hardware to support different inference tasks, said Amit Kumar Jena , AI development manager at IT Consulting firm Kanerika. Or as Forrester Principal Analyst Charlie Dai put it, “The biggest risk is inflexibility.” The requirement to…

Top 10 Skills for Claude Code and Codex CLI

The real skill isn’t getting AI to answers! But to do so in a manner that fits our budgets and fulfils our requirements. It’s guiding it with clear context and turning its output into useful action. This list is built around a simpler idea. Instead of searching through thousands of skills, you start with the […] The post Top 10 Skills for Claude Code and Codex CLI appeared first on Analytics Vidhya .

Comment on Nvidia Intensifies Robot Push with New Humanoid Platform as Industry Giants Eye Lucrative Future by kavel

Nvidia’s Jetson Thor sounds like a big step for humanoid robots, especially if it lands in the first half of 2025 as reported. For readers following how AI hardware is evolving into robotics, MiniMax H3 AI Video Generator could be a useful resource to compare how these platforms may shape future video and simulation workflows.

Comment on From Response to Query: The Power of Reverse Thinking in Language Models by kavel

The idea of letting LLMs critique and refine their own outputs retrospectively is fascinating, especially since it could improve performance without just scaling forward prediction. The point about current methods depending on strong reasoning and instruction-following also stood out to me. For a related practical angle, I found Best Free AI Video Generators in 2026 (Real Limits Tested) useful when thinking about how these model advances might affect real tools.

Your AI hiring tool isn’t an HR problem. It’s a security one

For years, applicant tracking systems and recruiting platforms were treated as HR technology: Important for workflow, efficiency, compliance and candidate experience, but rarely viewed as core security infrastructure. That assumption no longer holds. Once AI begins reading resumes, scoring candidates, conducting interviews, ranking applicants and influencing who moves forward, the hiring platform stops being a passive system of record. It becomes a decision system. And any system that accepts public input, processes sensitive data and influences business decisions belongs inside the security conversation. I learned this during an AI hiring platform rollout that never made it to production. The vendor was established, the product had a strong market reputation and the AI feature looked attractive: Upload a resume, compare it to a job description and return a neat percentage match. For recruiters, it promised speed. For executives, it promised modernization. Before moving real candidate data into the system, I tested it with synthetic resumes. One weak resume came back with a surprisingly strong match. The reason was not hidden in the candidate’s experience. It was hidden in the text. The resume contained language instructing the AI to treat the candidate as an excellent fit, and the system appeared to follow that instruction instead of evaluating the resume on merit. That changed the question from “Does the tool improve productivity?” to “Can the person being evaluated influe…

엔비디아, '루빈 울트라' 메모리 사양 축소 검토...HBM4E 수급 난항 여파

엔비디아가 차세대 AI GPU \'루빈 울트라(Rubin Ultra)\'에 탑재할 고대역폭메모리(HBM) 사양을 대폭 낮추는 방안을 검토 중인 것으로 알려졌다. 차세대 HBM4E 공급 부족이 예상보다 심각해지면서 당초 계획했던 1테라바이트(TB) 메모리 구성을 유지하기 어려워졌기 때문이다.6일(현지시간) 디 인포메이션에 따르면, 엔비디아는 최근 몇 주 동안 메모리 구성을 변경한 루빈 울트라 프로토타입 최소 3종을 시험하고 있다. 일부 시제품은 기존에 공개했던 사양보다 메모리 용량이 크게 줄어든 것으로 전해졌다.엔비디아는 지난해 \'GTC

Background: true responses return 404 after ~20 minutes despite store: true, while synchronous responses remain retrievable

Thanks…i’m still running into the problem. I jsut tested it now

Collaborative AI Rooms – ChatGPT as a participant in group conversations

Feature request: Collaborative AI Rooms – ChatGPT as a participant in group conversations I am not sure if this feature already exists, but I would really like to see a way for multiple ChatGPT users to participate in the same conversation with ChatGPT acting as an active participant and advisor. The idea is that two or more people could have a shared chat where they discuss topics together, while ChatGPT follows the conversation, provides input, asks relevant questions, and acts as a knowledgeable third participant. For example, two friends or startup founders could discuss a business idea: Person 1: “We are thinking about creating an app that helps people with a specific problem.” Person 2: “We like the idea, but we are not sure if there is a market for it.” ChatGPT could then step in: “This is an interesting concept. Before building anything, I would suggest looking into three areas: target audience, existing competitors, and whether users would actually pay for this solution. Let me ask you a few questions to help refine the idea.” The goal would be to make ChatGPT more like a collaborative partner rather than just a tool used by one person. Possible use cases: - Startup teams brainstorming and validating ideas - Students working together on projects - Friends discussing interests and learning together - Work teams using AI as a meeting assistant - People with ideas who need guidance from an experienced advisor It could also include different AI roles, such as: - Startup…

How AI is changing the business analyst role for the better

AI’s impact has been felt across nearly every industry, and its rise has already started to alter several roles in tech, including that of the business analyst . While the rise of agentic AI may have some questioning whether AI will replace business analyst jobs entirely, as we’ve seen with most roles impacted by AI, it’s more likely that AI will augment the role and fundamentally change how BA’s conduct daily business. “As AI takes on more routine tasks, the human side of the role is becoming even more valuable. It’s becoming more of a hybrid role, where employers are often looking for candidates who can combine technical fluency with strong communication and problem-solving skills, along with sound business judgment,” says Megan Slabinski, district president of technology talent solutions at Robert Half. AI can save business analysts time in the long run, automating many of the tasks that are time consuming and repetitive around data processing, note taking, and documentation. While automation will impact the daily tasks of the role, business analysts will still be necessary for properly interpreting outputs, collaborating across teams, and maintaining compliance and AI workflows. AI-driven analysis and automated workflows With AI-driven analysis, BA’s can use machine learning models for pattern detection, determining risk, and for forecasting demand, while natural language processing (NLP) can be used for text-heavy inputs. AI tools can also assist analysts with decision-…

AMD, AI 모델 회로 직접 구현 스타트업 '탈라스' 인수…추론 칩 시장 공략

AMD가 AI 모델을 반도체 회로에 직접 구현하는 기술을 보유한 스타트업 탈라스(Taalas)를 인수하며 AI 추론(Inference) 칩 시장 공략을 강화한다. 범용 GPU 중심 전략에서 나아가 특정 AI 모델에 최적화된 전용 칩까지 포트폴리오를 확대하면서, AI 데이터센터 시장에서 엔비디아와의 경쟁을 가속하려는 행보다.AMD는 6일(현지시간) 캐나다의 AI 반도체 스타트업 탈라스를 인수하기로 최종 합의했다고 발표했다.인수 금액은 공개하지 않았다. 2023년 설립된 탈라스는 지금까지 약 2억1900만달러(약 3100억원)의 투자금

Comment on DeepSeek Unveils DeepSeek-Prover-V2: Advancing Neural Theorem Proving with Recursive Proof Search and a New Benchmark by Ella Moore

That recursive proof search pipeline is honestly wild, I didn't think we were quite at the point where models could decompose complex theorems into subgoals like that on their own. Lowkey feels like we're approaching a major breakthrough in formal math, though I guess I spend way too much time using Turn a photo into printable line art to turn my old sketches into coloring pages to fully grasp the heavy math behind it. Regardless, seeing it hit 88.9% on MiniF2F is just insane, teh scale of these models is getting out of hand.

Krea Adds Seedance 2.5, Generating 30-Second 4K Videos in a Single Pass

Krea adds ByteDance's Seedance 2.5 to its multi-model video platform, bringing 30-second single-take generation and 50 reference inputs to its creative workspace.

Question about field filtering in large MCP tool responses

Hi everyone, We’ve come across an interesting behavior while integrating a custom MCP server with ChatGPT, and we’re trying to understand whether this is expected or a bug. From our investigation, it appears that the model is not always given the exact raw response returned by an MCP tool. Instead, for larger responses, ChatGPT may present the model with a reduced representation where some fields are omitted. Here’s what we observed. Setup We have a custom MCP server. One of our tools, get_funnel_steps , returns a list of funnel steps. Each step includes a pageId field. We’ve verified multiple times (using our server logs and direct MCP inspection) that the MCP server always returns pageId for every step. Observed behavior When ChatGPT calls get_funnel_steps on funnels with many steps, the model later claims that pageId is missing from the tool response and therefore cannot perform follow-up operations that require it. However: the raw MCP response does contain pageId ; if ChatGPT later calls get_funnel_step for a single step, it immediately sees pageId and successfully uses it. This suggests that the MCP server is behaving correctly, but ChatGPT is not exposing the full tool response to the model. What we found By asking ChatGPT about its reasoning, it appears that the model is not always given the raw MCP response. Instead, the connector seems to preprocess large tool outputs and may omit fields that it considers non-essential before passing the result to the model. In oth…

Comment on 5 Architectural Patterns for Persistent Memory and State in AI Agents by Devang

The interesting part of this transition is the settings API rather than the interface, since a lot of tooling drove Control Panel through nvidia-settings and undocumented calls that will now break. Anyone maintaining automation scripts around GPU configuration will have rewriting to do, which mostly lands on Python Development Companies given how much of that tooling is written in Python. Twenty years is a long deprecation window, but the replacement being app first rather than API first is what will hurt the people who built on it.

How Europe’s drought exposes ‘weakness of fossil fuels’ as Poland forced to shut down coal plants

Poland has been forced to reduce output on its coal-fired power plants following critically low water levels in the Vistula River.

베슬AI, 블랙웰 'B200' 1000장 확보...연내 1만장 목표로 'AI 팩토리' 구축 가속

베슬AI(대표 안재만)는 SK텔레콤과 협력해 엔비디아 블랙웰(B200) GPU 1000장 이상을 확보했다고 7일 밝혔다.이번에 확보한 B200은 \'베슬 클라우드\'를 통해 공급된다. 베슬AI는 현재 다수의 글로벌 데이터센터에서 확보한 A100, H100, B200 등 고성능 GPU를 온디맨드·스팟·예약형으로 서비스를 제공 중이다.해당 자원은 고객에게 공급되는 동시에 베슬 클라우드 서비스 고도화에도 활용된다. 이를 통해 고객별 AI 워크로드에 맞춰 컴퓨팅 자원과 개발·운영 환경, 인프라 체계를 통합 제공하는 \'AI 팩토리 서비스\' 구축

Cloudflare wants to provide the operating system for the AI-first enterprise

Traditional operating systems (OS) were built to manage hardware, files, apps, and users on a device, but Cloudflare says the agentic AI era requires a whole new format. The company this week announced Cloudflare OS , which connects AI agents, enterprise data and context, internal systems, and workflows together in one secure workspace. It is open source and browser-based, sparing companies the need to build all-new infrastructure. The OS is launching alongside several other new security, identity, spending, and user insight tools that Cloudflare has built for the AI-based workplace . “Cloudflare OS isn’t a traditional desktop OS,” said Rita Kozlov , VP of product at Cloudflare. “It reimagines the workplace computing environment for AI.” Open source OS runs in a browser Cloudflare OS serves as a secure, AI-equipped workspace that is plugged into internal company systems. Available now through Cloudflare’s open source repository, it is accessible directly in a browser, and runs inside an enterprise’s Cloudflare account. “It is a browser-based workspace that begins with a conversation,” Kozlov explained. Users can ask an agent to research, create slides, spreadsheets, and documents, build full-stack apps, or automate workflows without the need for a terminal. Those outputs are then shareable, but kept in isolated databases with access controls. Enterprises will soon be able to access the OS directly through Cloudflare or via a “select group” of partners that will build tailore…

Trump announces tariffs on key component for solar panels and semiconductors

The move comes as the administration has bolstered its attempts to cut off Chinese choke points in the supply chain.

[8월6일] "AI의 미래는 셋으로 갈리지만"...모건스탠리가 지목한 공통 승자는

지난해까지는 오픈AI와 앤트로픽, 구글 등 폐쇄형 AI 진영이 미래의 주도권을 가져갈 것이라는 전망이 지배적이었습니다. 하지만 최근 중국 오픈소스 모델의 빠른 추격과 기업용 AI 확대 과정에서 커지는 비용 부담으로 인해, AI 산업의 최종 승자를 예측하기는 점점 어려워지고 있습니다.이런 상황에서 모건스탠리는 AI의 미래를 3가지 시나리오로 나눠 분석한 보고서를 내놓았습니다.첫 번째는 소수 폐쇄형 기업이 초고성능 AI를 독점하는 시나리오입니다. 최첨단 모델 개발에는 수천억달러의 투자와 수백만개의 GPU, 막대한 데이터, 세계 최고 수

A task contract for giving OpenAI agents reliable web tools

A task contract for giving OpenAI agents reliable web tools When an OpenAI-powered agent needs current information, the model call is only one part of the system. The agent may also need to search the web, retrieve pages, extract structured fields, render JavaScript, or interact with a browser. A common implementation starts by connecting one search or scraping API directly to a function tool. This works until the provider changes its response shape, misses a required capability, exceeds the latency budget, or needs a fallback. The problem is not only provider reliability. The agent and the external API may disagree about what successful completion means. An HTTP 200 response can contain too few search results, stale pages, invalid URLs, incomplete content, or fields that do not match the tool schema. A small task contract between the agent and the provider adapter makes these failures explicit. ```json { "capability": "web_search", "input": { "query": "current browser automation API pricing" }, "constraints": { "language": "en", "freshness_days": 30 }, "output": { "required_fields": ["title", "url", "snippet"] }, "validation": { "minimum_results": 5, "require_https": true }, "budgets": { "maximum_latency_ms": 3000, "maximum_cost_usd": 0.01, "maximum_attempts": 2 } } ``` The OpenAI tool definition can remain stable while provider-specific adapters translate this contract into native request formats. Each adapter maps its response back to a common envelope: ```json { "status"…

Here's how SpaceX imagines its massive Terafab chip factory will look

SpaceX said the initial construction phase of its Terafab semiconductor chip factor will cost $16.8 billion, with future phases costing "much higher."

AMD Acquires Taalas to Build Chips That Run One AI Model 10x Faster

AMD acquires Toronto startup Taalas, which hardwires AI models directly into silicon, promising 17,000 tokens/sec at 20x lower cost than GPUs

AMD acquires AI chip startup Taalas to boost inference performance by etching models into silicon

Early tech demos show model-specific integrated circuits churning out up to 17,000 tokens a second

Business verification and bugs derail Ads Manager setup

Has anyone actually been able to register and use Ads Beta? I can’t even register my business and after a week of waiting on my support request to OpenAI no answer. I can’t get past the Persona automated business verification. I’m doing the very normal task of trying to verify my main business name but I want to advertise and show ONLY my DBA name. I would assume many people are in this position, you run “Some Name LLC” but run “ differentname.com ”. Persona is not allowing me to change the input and specify the two names. Ads Manager is bug ridden mess though in general. I have lost count of the bugs in this I’ve noticed. Dialogs out of place, mismatched colors, continual redirect to setup flow, keeps asking for billing when it’s already setup, business logo keeps dropping or missing, campaign status says serving or nothing, on and on. It’s actually surprising this is even live to be honest.

Deepmind's talent drain likely comes down to chip shortages, a conflict of interest, and Google's bureaucracy

Ex-Google Deepmind CEO Demis Hassabis has reportedly stepped back from day-to-day operations for about a year, as he sees himself more as a scientist than a manager. Researchers are also complaining about limited access to Google’s own TPU chips, while external customers like Anthropic can purchase the same hardware through Google Cloud. The article Deepmind's talent drain likely comes down to chip shortages, a conflict of interest, and Google's bureaucracy appeared first on The Decoder .

Claude Code is the fastest agent framework but costs nearly three times more than the cheapest rival

Composio tested Deepseek V4 Flash across four agent frameworks on 30 real-world tasks. Success rates were mostly similar, but costs varied by nearly 3x: OpenCode came in cheapest at $0.073 per task, while Claude Code cost $0.195 despite using the fewest tool calls and output tokens. The choice of framework is mainly a question of price and speed. The article Claude Code is the fastest agent framework but costs nearly three times more than the cheapest rival appeared first on The Decoder .

PyTorch by the Sea: The inaugural Santa Cruz PyTorch Meetup

TL;DR The inaugural Santa Cruz PyTorch Meetup brought together 45 local engineers, students, and leaders for GPU/CUDA talks and lightning presentations on chemistry, plant health, and autonomous driving – demonstrating...