RAG
200 articles tagged with this keyword, sorted by most recent first.
The case and model for real-time AI cost visibility at the infrastructure layer
I spend most of my time inside other companies’ engineering teams, building systems to track and optimize AI spend. The conversation almost always starts the same way. Someone pulls up a dashboard, points at a number bigger than it should be, and says some version of “we know it went up; we just can’t tell you why.” I use an analogy for it: AI-era CIOs are like city planners optimizing a busy intersection. They can measure the volume and hear the pleas to fix congestion, but can’t tell whether a vehicle is a truck or a bike, or why it’s on the road. Without that, they can’t design the right fix, so they build a highway at great expense when the data would show all it needed was a bike lane. Every model choice and budget conversation happens against that blurry picture, and the traffic gets heavier every quarter. Gartner expects worldwide AI spending to grow 47% this year , with agentic AI software up roughly 141%. By 2028 , it projects an average Fortune 500 enterprise will run over 150,000 agents, up from fewer than 15 in 2025. Why the cloud playbook can’t answer the AI question AI presents a fundamentally different problem than cloud cost management, where we answered, “whose spend is this?” largely by tagging the resource. A VM has an owner, a bucket belongs to a team and FinOps optimizes from there. Billing was slow but acceptable, because spend moved inside predictable bands, a human provisioned each resource before it cost anything, and governance capped how fast costs…
Farage’s victory over Binface won’t silence his critics
The Reform UK leader wanted to send a "people versus the establishment" message in Clacton. But he'll face continued scrutiny of his finances — and questions over his judgement.
Farage wins UK by-election to return to parliament
British politician Nigel Farage retains his seat in parliament after resigning last month over an investigation into his finances that triggered a by-election.
Farage wins Clacton by-election
Reform UK leader sails home in special contest he triggered amid scrutiny of his finances — but misses the count.
How AI is helping to open a new chapter for ancient Chinese literature
For generations, working with ancient Chinese texts has been a painstaking task. Specialists would spend years deciphering unfamiliar characters, copying manuscripts by hand, adding punctuation to texts written without it, comparing different editions and tracing references scattered across thousands of years of records. But artificial intelligence is now changing that process, not only by helping to preserve fragile manuscripts but also by transforming how ancient texts are recovered, studied...
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.
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.
[Media Coverage] Interview with Deputy Director Naonori Ueda (NTT Facilities Journal, August 3, 2026)
Deputy Director Naonori Ueda’s interview article, “The Ability to Ask Questions in the AI Era: Defining New Roles for Humans and AI,” has been published in NTT Facilities Journal. For more details, please see <a href="https://www.ntt-f.co.j
Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
Implicit generative models are often trained adversarially, which can yield unstable dynamics and mode collapse. The invariant statistical loss (ISL) offers a fully sample-based alternative by comparing empirical ranks of real and generated samples. In this work, we formally characterize ISL as a proper divergence over continuous distributions and establish key regularity properties, showing that it is continuous and differentiable, thereby enabling stable gradient-based optimization without adversarial games. We further enhance ISL along two practical axes. First, to better model heavy-tailed data, where Gaussian latent priors can limit tail expressivity, we introduce Pareto-ISL, which replaces Gaussian noise with a generalized Pareto latent distribution to improve the representation of both typical and extreme events. Second, to handle multivariate data at scale, we propose ISL-slicing: a computationally efficient procedure that projects samples onto random one-dimensional subspaces, computes rank-based losses per projection, and averages them to capture high-dimensional structure. Experiments demonstrate improved tail fidelity with Pareto-ISL and show that ISL-slicing scales effectively to high dimensions. Specifically, in high dimensional settings we show that ISL can be used either as a standalone criterion or as a strong pretraining objective for subsequent adversarial fine-tuning.
Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
We investigate the convergence properties of popular data-augmentation samplers for Baye\-sian probit regression. Leveraging recent results on Gibbs samplers for log-concave targets, we provide simple and explicit non-asymptotic bounds on the associated mixing times (in Kullback-Leibler divergence). The bounds depend explicitly on the design matrix and the prior precision, while they hold uniformly over the vector of responses. We specialize the results for different regimes of statistical interest, when both the number of data points $n$ and parameters $p$ are large: in particular we identify scenarios where the mixing times remain bounded as $n,p\to\infty$, and ones where they do not. The results are shown to be tight (in the worst case with respect to the responses) and provide guidance on choices of prior distributions that provably lead to fast mixing. An empirical analysis based on coupling techniques suggests that the bounds are effective in predicting practically observed behaviours.
Near-optimal Delta-convex Estimation of Lipschitz Functions
This paper presents a tractable algorithm for estimating an unknown Lipschitz function from noisy observations and establishes an upper bound on its convergence rate. The approach extends max-affine methods from convex shape-restricted regression to the more general Lipschitz setting. A key component is a nonlinear feature expansion that maps max-affine functions into a subclass of delta-convex functions, which act as universal approximators of Lipschitz functions while preserving their Lipschitz constants. Leveraging this property, the estimator attains the minimax convergence rate (up to logarithmic factors) with respect to the intrinsic dimension of the data under squared loss and subgaussian distributions in the random design setting. The algorithm integrates adaptive partitioning to capture intrinsic dimension, a penalty-based regularization mechanism that removes the need to know the true Lipschitz constant, and a two-stage optimization procedure combining a convex initialization with local refinement. The framework is also straightforward to adapt to convex shape-restricted regression. Experiments demonstrate competitive performance relative to other theoretically justified methods, including nearest-neighbor and kernel-based regressors.
"Onboarding" users with custom prompts
This is 100% what is needed. Without some sort of initial prompt to start users most people aren’t able to figure out how to use our plugin.
Reform UK leader Farage faces parody candidate in by-election
Farage will likely win, but at some cost to his reputation.
Idea to make ai more sustainable
Renewable-Powered Thermal Storage for Data Centre Cooling I have an idea for reducing the environmental impact of cooling AI data centres. Data centres produce a large amount of heat and can require significant amounts of water and electricity for cooling. My idea is to use reusable phase-change materials as thermal storage instead of relying as heavily on conventional cooling methods. Solar panels and wind turbines could provide renewable electricity to power chillers. These chillers would cool the phase-change material, which could then absorb heat from servers through a closed cooling system. The cooling material would remain separate from the electronics, while a heat-transfer fluid would move heat between the servers and the thermal-storage system. When renewable electricity is readily available, the system could recharge the thermal storage by cooling the material again. This would create a reusable cycle rather than continuously requiring fresh cooling resources. The system could potentially be combined with recycling programmes for old computer hardware, allowing useful materials from retired servers and electronics to be recovered and used in manufacturing new equipment. I understand that this idea would require significant engineering research to determine whether it is practical, cost-effective and more energy-efficient than existing cooling systems. However, I think renewable energy, thermal storage, efficient cooling and hardware recycling could work together to…
Polls close in UK by-election with Nigel Farage and Count Binface going head-to-head
Vote counting has already begun and will continue through the night with a result expected by around 7:00 am local time on Friday.
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…
Give Google the boot by building your own search engine
Got $10 and a gig of storage space? Marlin lets you weight your crawl toward the parts of the web you actually care about
Building a real-time AI RPG with evolving narratives using LLMs
I am working on something similar for a while. Have GitHub pages describing the project (not yet published). I am unable to paste here project pages describing the project in detail (GitHub static pages) So, the pipeline is getting quite a complexity using orchestrated main pipeline, RAG for lore, history where I use conversation semantic search, memories, etc. Tiered memory system with gradual compression, subagents to act on behalf of active NPCs, weather simulation, map support and detailed scene etc. tracking. Works reasonably well, but it is quite token demanding (using ollama cloud mostly) and lazy (1 turn about 60-90 seconds as of now)
Docusign alternatives for teams: what to look for (and what to avoid)
Choose Nitro Sign for predictable pricing & fewer surprises Free Trial Docusign may be the most widely used eSignature platform, but many teams run into the same issue as they scale—the more documents they send, the harder it is to predict what they’ll actually pay. The risk of high overage costs paired with complex pricing structure is driving teams to look for an alternative to Docusign in 2026. This buyer’s guide breaks down why those challenges matter, what to look for in an eSignature solution, and how leading Docusign alternatives—like Nitro Sign —compare for teams that send a high volume of documents but want simpler pricing and fewer surprises. Nitro Why some teams are looking for alternatives to Docusign Three trends drive teams to look beyond Docusign: Bills that increase unpredictably as usage grows Difficulty understanding what features are included vs. which are paid add-ons Pricing models that don’t scale well with high document volume Here’s a more detailed look at each of these challenges. Docusign bills become unpredictable as usage grows Many organizations encounter unexpected costs as usage increases. Envelope limits, counting methods, and per-transaction pricing can quickly turn a straightforward Docusign subscription into an unpredictable expense. Impacts may include: Budget overruns tied directly to business growth Paying for documents that are sent but never completed Difficulty forecasting total cost at scale Docusign pricing doesn’t clearly indicate…
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!
ChatGPT Business account did not receive the Codex 15M-user hard reset
Yeah, I get the feeling that business users are second class citizens all the time. With resets. But also with rollout of new features, e.g. the Live voice function. Sad.
Comment on NVIDIA’s Minimal Video Instance Segmentation Framework Achieves SOTA Performance Without Video-Based Training by simsownersdetails
For users looking into SIM registration information, this guide provides a helpful starting point. Learn more through Sim Owner Details and explore the available information. Sim Owner Details can help you understand SIM registration and ownership verification processes while providing useful information for mobile users seeking clear guidance.
Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model
Enterprise Document Intelligence [Vol.1 #9ter] - The pipeline from Article 9 calls a model at several steps to be sure it is right. On easy questions that is needless latency. A per-question signal routes them past the model, about two seconds saved for a keyword match. The post Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model appeared first on Towards Data Science .
The "remembered" feature, a concept after I was tired asking "hey got can you REMBER this" it forgets so easily
hey I’m currently using gpt to open this support page I just want to tell a new feature called “Remembered” storage section, it’s like when a user signs in or has been in a longe time, they ether get 250GB or 120GB depends on you and this was just my size reomendedbut for not signed in users they get around 4GB to 5GB of “Remembered” chat, they can open it in the settings or in the ui side bar, or custom way if you want to open it in a specific way, but the parts where they told gpt to “remember” it gets putted there, now for inactive users that hasn’t in there for days or years, you can send them a warning that they will get their remembered chat or account gets remove, you can give them how much days as it depends on you, but please make this as a feature, it would go great
US clean energy investment surges 22% in second quarter
The results, published by the Clean Investment Monitor, were largely driven by residential battery storage installations.
Lost jobs, inequality, rogue agents: why are we accepting oligarchs’ AI agenda? | Robert Reich
The dangers of AI become clearer every day. Why are we still acting as if we have no choice about our future? Rather than producing jobs, the US economy actually lost 23,000 jobs in July, according to Bureau of Labor Statistics data released on Friday. In addition, May and June’s job numbers were revised downward, showing a combined 103,000 fewer jobs than previously reported. As if this weren’t bad enough, wage growth has also slowed. Average hourly earnings rose by just 0.1% from June. Continue reading...
heise-Angebot: Konferenz zu Storage, Server, Network: S2N-Tickets noch zum Frühbucher-Preis
Server- Storage- und Netzwerkexperten treffen sich am 3. und 4. November in Berlin. Bis zum 14. September gibt es noch Early-Bird-Tickets.
Comment on Microsoft’s LongNet Scales Transformer to One Billion Tokens by Suzy Lee
Whether you enjoy racing, sports, action, or adventure, boy games provides entertaining options to keep every gaming session exciting.
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.
Why AI models need a real-time web intelligence layer
A growing sentiment in tech is that large AI model providers will eventually replace traditional software vendors. The argument makes some sense. If a model can write code, answer questions, and automate workflows, then over time it should be able to take on the functionality of thousands of existing applications. Why maintain a fragmented stack of software tools and solutions when a single intelligent system can do it all? But as enterprises attempt to move to production, it all starts to break down. Why? Because models are powerful, but they’re not self-sufficient systems. Organizations are finding that a major limitation of modern AI is the lack of infrastructure to reliably access the world’s information. There are limits to model-centric thinking Over recent years, large language models (LLMs) have made significant advances in reasoning, generation, and task execution. They can be extremely useful for summarizing documents and generating insights. At times, they can orchestrate complex workflows. When in controlled environments, they look capable of replacing entire categories of software. But these capabilities depend heavily on an oft-overlooked factor: access to external information. AI models operate on static training data and probabilistic reasoning. Without continuous access to up-to-date information, they can’t reliably answer questions about things like current events or what market conditions are like today, not yesterday. Retrieval-augmented approaches have a…
The vendor consolidation trap: When one throat to choke costs more than it saves
Vendor consolidation is sold as discipline. Fewer vendors, simpler architecture, better pricing through volume, one throat to choke when something breaks. Every one of those benefits is real on paper. The problem is that the biggest cost of consolidation rarely appears on the slide the procurement team uses to sell it internally, and it does not show up on the savings tracker until the first renewal cycle after the ink is dry. Within CIO Mastermind’s topic-specific cohorts, which I sometimes facilitate, I hear a version of the same story often enough to recognize the pattern early. A consolidation program gets pitched against a strong multi-year savings target. The first year or two look good. Then a renewal arrives, the remaining vendor prices to the switching cost the company just built for itself, and a meaningful share of the projected savings quietly erodes. The company still ends up with fewer vendors. It does not always end up with the leverage the original business case promised. What consolidation actually removes What consolidation actually removes is competitive pressure on the vendor you keep. That is the part most business cases leave out. Going from a dozen vendors in a category down to three or four feels like simplification, and it is. It is also a message to the vendors you kept about how expensive it would be for you to leave. The fewer live alternatives you maintain, the more accurately a vendor can price to your captivity rather than to the open market. A…
Comment on CMU’s Novel ‘ReStructured Pre-training’ NLP Approach Scores 40 Points Above Student Average on a Standard English Exam by mistfallhunterwiki
The idea of pretraining on restructured data is fascinating, especially how the QIN system reportedly scored 40 points above the student average on the Gaokao-English Exam while using only 1/16 of GPT-3's parameters. That efficiency gain makes the RST paradigm particularly compelling for broader NLP applications, not just exam benchmarks. I appreciate the authors sharing this direction and look forward to seeing how restructured pretraining performs across other language tasks.
Comment on Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models by Video Maker Ai
Create studio-quality videos in seconds with the most powerful AI video maker
OnDemand AI apps to run across Qualcomm platforms
OnDemand agents to power Dragonwing chipsets on edge devices
Balkans’ Trump lobbying heats up
Bosnian Croat leader Dragan Čović has tapped Trump-linked firm Mercury Public Affairs to lobby for his party.
‘Absolutely unacceptable’: Putin enrages Japan with trip to disputed island
The Kuril archipelago has repeatedly fueled tensions between Tokyo and Moscow.
'큐원' 만든 린쥔양, AI 에이전트 스타트업 출범…대규모 투자 유치 성공
\'큐원\' 개발을 주도했던 핵심 연구원 린쥔양이 알리바바를 떠난 지 반년도 채 되지 않아 AI 스타트업을 설립하고 빅테크와 벤처캐피털(VC)로부터 대규모 투자를 유치했다.린쥔양은 12일 X를 통해 상하이에 AI 에이전트 전문 프래그마틱 랩스(Pragmatik Labs)를 설립했다고 발표했다.이번 투자 라운드는 중국계 대형 VC인 가오롱 벤처스와 홍산(HSG)이 공동 주도했으며, 알리바바의 경쟁사인 텐센트 홀딩스 등 주요 투자자들이 참여했다. 블룸버그에 따르면, 이번 자금 조달 과정에서 프래그마틱 랩스의 기업 가치는 20억달러(약 2조
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.
Why are support cases and feedback threads being closed?
yeah but sadly some have hearts like this one
Urban Company shares rise 4% after Rs 428 Cr block deal
Home services marketplace Urban Company witnessed a block deal worth around Rs 428.4 crore, involving 3.15 crore shares or 2.13% of the company's total share capital. According to NSE block deal data, 3.15 crore shares of the home services platform changed hands at Rs 136 per share. SBI Mutual Fund reportedly is the buyer, while Accel and VY Capital are the sellers. SBI Mutual Fund has been steadily increasing its exposure to Urban Company. The mutual fund had acquired an additional 4% stake in the company through bulk and block deals worth Rs 632 crore in March 2026. Its stake in the company stood at 6.59% before the latest transaction. Prior to the latest transaction, Accel held a 6.99% stake in Urban Company, while VY Capital held around 12.9% through four entities: Vyc11 Limited, Vyc23 Limited, Dharana Fund, L.P., and Dharanauc Limited, according to the company’s shareholding data. The latest development comes after global brokerage Morgan Stanley upgraded Urban Company to “ Overweight ” from “Underweight” and sharply raised its target price to Rs 165 from Rs 128 In Q1 FY27, the company reported a 44% year-on-year growth in operating revenue to Rs 528 crore . However, it slipped into a loss of Rs 92 crore during the quarter amid continued investments in its InstaHelp business. The latest transaction boosted investor sentiment, with Urban Company’s shares rising over 4% to around Rs 144.6 as of 11:25 AM on Thursday. At this level, the company’s market capitalisation stood…
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...
Codex Rate Limits Discussion Thread
Look, I used maybe 3–4 commands and I’ve already hit the weekly limit. This is honestly getting ridiculous — I can’t get any work done like this. I can’t afford to keep paying more and more, and my quota seems to run out faster every time… FOR THE LOVE OF GOD, PLEASE GIVE US A HIGHER LIMIT!
AI agents are turning data silos into an existential infrastructure problem
Enterprises have built their data systems for humans, but AI agents need a whole new infrastructure. Separate research from Cloudera and Google/MIT found that, not surprisingly, there is fervent enterprise interest in AI agents, but underlying infrastructure struggles to keep up. Deployments continue to be hampered, sometimes even abandoned, largely due to issues with data access, context, and governance. “Enterprise adoption of agentic AI is on the cusp of an extraordinary acceleration,” the Google/MIT report noted . “As organizations look to scale agentic AI across the enterprise, they cannot ignore their data systems.” Resolving data bottlenecks, then, should be an immediate priority. Projects delayed, inaccessible data Cloudera’s report , created in partnership with Wakefield Research, describes the need for a “great AI re-architecture.” Of the 1,500 enterprise architects and cloud infrastructure leads surveyed, a stunning 95% said they had delayed or cancelled AI projects, in some cases six or more, in the past year, due to issues with data governance, compliance, or regulatory issues. A wide majority also reported that AI integrations have changed their data storage and architecture practices, AI workloads have increased infrastructure costs, and current data architecture requires a “significant overhaul” to meet AI goals. “Even if enterprises are ready to use AI, many are coming to the realization that the foundational infrastructure it relies on is not,” the report n…
Farage paints himself as local Clacton hero in battle to beat Binface
Analysis of Reform UK’s £10,000 spend on online ads in Clacton by-election reveal a mix of anti-establishment rhetoric and more prosaic matters.
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…
Kruel.ai KV2.0 - KX (experimental research) to current 8.2- Api companion co-pilot system with full modality , understanding with persistent memory
Pretty excited. A potential Future outcome signed an NDA with Kruel.Ai Inc. recently, and they will get access to the full end to end workings and math model to look at. Its been under a trial for about 3 months now. More excited to see what this Tech company thinks about it, not Everyday someone gets to learn how the Blackbox thinks under the hood of a system that is not like the others Lets see where this Adventure goes. updates have slowed down a little because there has been a lot of things going on with this around around me that has had me pretty nailed down. Meeting all the Company Partners next week This should be fun.
Feature Request: Age-Verified Adult Mode for Creative Writing and Long-Term Roleplay
I would like to request an optional adult mode for age-verified users, especially for creative writing and long-term roleplay. I fully support strong protections for minors. However, I believe there should be a clearer distinction between underage users and verified adults, so that adults can choose a broader range of mature fictional expression without weakening protections for younger users. My main use case is long-term character roleplay and collaborative fiction. Over time, ChatGPT can build extremely nuanced character dynamics, emotional continuity, relationship history, and consistent characterization. That is one of its greatest strengths. However, when a fictional relationship becomes more physically intimate, the available expression suddenly becomes much more restricted. This creates an unusual user experience: the character writing, emotional nuance, and relationship development may be excellent, but the story must either fade out abruptly or be moved to another service. Moving to another model or platform often means losing the carefully developed characterization and relationship continuity that made the roleplay compelling in the first place. For adult users who have completed age verification, I would appreciate an optional mode that allows a wider range of consensual mature fictional content while still maintaining clear safeguards against harmful, illegal, exploitative, or underage sexual content. A system like this could include: Strict age verification An…
David Protein founder's candy launch is full of AI typos. The brand's president says it's intentional 'rage bait.'
"We left them there for a little bit of internet rage bait," HallPass president Michael Tierney told Business Insider. "It's worked."
Three insights you may have missed from theCUBE’s coverage of the Neo4j GraphTalk event
Graph intelligence is fast becoming the enterprise’s missing connective tissue — the knowledge layer that lets models move from clever prototypes to reliable, decision‑grade systems. By preserving relationships across fragmented data, it gives artificial intelligence the context needed to produce more accurate answers and support informed action. At the Neo4j GraphTalk event, the conversation wasn’t […] The post Three insights you may have missed from theCUBE’s coverage of the Neo4j GraphTalk event appeared first on SiliconANGLE .
1 Million Context to enable professional workloads
The part that irritates me the most about this is that when these models come out, their context limit is marketed like it’s real.
The Gender War Over ‘Obsession’ Has Come for Theme Parks
Debate over the true villain in Obsession is raging again after Universal Studios announced the movie’s female lead will be featured at an upcoming Halloween event.
Custom / Reorder pinned chats
Title: Feature Request: Ability to Custom Reorder Pinned Chats in Sidebar Description: Currently, pinned chats in ChatGPT sort themselves based on the most recent activity or the order in which they were pinned. For users who rely on pinned chats to manage ongoing projects, recurring workflows, or daily prompts, this automatic ordering can be disruptive. I would like to request the ability to custom-reorder pinned chats in the sidebar. Suggested Functionality: Drag-and-Drop: Allow users to click, hold, and drag pinned chats up or down to set a permanent, manual order. Static Positioning: Once a user sets the order, it should remain fixed regardless of which pinned chat was updated or used last. Visual Dividers (Optional): The ability to group pinned chats or separate them would add even more value. Why this is helpful: This improvement will greatly enhance productivity for power users. It allows us to organize our sidebar by priority, project phase, or frequency of use, creating a much cleaner and more predictable workspace.
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 .
Video Funktion einbauen für bessere Nutzung
Ihr könntet vielleicht eine Video Funktion einbauen. Das wäre viel Sinnvoller, anstatt man mehrere Screenshots machen muss und ChatGPT fragen muss, wenn man etwas nd versteht.
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.
Rent in Spain tops €1,100 for 80 m²: these are the priciest and cheapest cities
Renting an 80-square-metre home in Spain costs an average of 1,131 euros a month, but regional disparities are stark: from 600 euros in Jaen to 1,610 in San Sebastian.
Queen’s Brian May outraged by AI version of album cover and tells UK PM Andy Burnham: ‘You must stop it now’
"Maybe this is the time to realise WE ACTUALLY DON'T NEED AI," Brian May declared when posting a ridiculously poor version of what AI thinks one of Queen's albums looks like. He has urged UK Prime Minister Andy Burnham to block AI "before it's too late".
IEA and OPEC split on global oil demand estimates as Strait of Hormuz closure drags
The world's two most watched oil forecasters published sharply conflicting views of 2026 on Wednesday, with the IEA deepening its call for the first annual demand decline since the pandemic while OPEC still sees consumption growing, as the closure of the Strait of Hormuz drags on.
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.
Google’s Pixel 11 lineup offers fewer hardware changes, but much more Gemini
The Google Pixel 11 series starts $100 costlier than the last year, but offers 256GB base storage.
How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS
Learn how OneAdvanced, a UK enterprise software provider, built a UK-sovereign AI platform by self-hosting Llama 4 Maverick and Llama Guard 4 on Amazon SageMaker AI, with a RAG pipeline on pgvector and over 50 agents built with Strands Agents SDK on Amazon ECS.
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.
Fragrant fortunes: Doha's Teeb Al Hazm plans London debut as Gulf perfumes gain wider appeal
From oud and bukhoor to niche fragrance houses, the Teeb Al Hazm perfume exhibition reflects the Gulf's enduring love of scent as organisers prepare to take the concept to Europe.
Comment on Can GRPO be 10x Efficient? Kwai AI’s SRPO Suggests Yes with SRPO by Madison Miller
SRPO sounds interesting, especially if it can reduce reinforcement-learning post-training costs without sacrificing reasoning performance. I came across aviator while reading about newer approaches to LLM training and found the idea of history resampling particularly interesting. Improvements in training efficiency could make advanced reasoning models much more practical to develop and iterate on. I’d be curious to see whether the same gains hold across broader tasks beyond math and coding.
Expert explains: Why TRAI’s 80% capacity rule for 5G slicing may backfire on ordinary users
TRAI’s proposed 80% rule for 5G network slicing could incentivise telcos to throttle regular users instead of adding capacity, telecom expert and former Qualcomm executive Parag Kar warns. The post Expert explains: Why TRAI’s 80% capacity rule for 5G slicing may backfire on ordinary users appeared first on MEDIANAMA .
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,…
Algeria Approves Inter-Ministerial AI Roadmap Built on Sovereign Open-Source Models and National Compute
Algeria has agreed a joint inter-ministerial roadmap to deploy artificial intelligence across public services, built around sovereign open-source AI models, domestic high-performance computing and national data storage — a bet on technological independence that diverges from the routes its North African neighbours have taken. Higher Education and Scientific Research Minister Kamel Baddari said the deployment [...]
What successful AI centers of excellence actually do: Lessons from real enterprise implementations
Most articles about AI Centers of Excellence (CoEs) focus heavily on organizational structures, steering committees and high-level governance models. They explain why enterprises need an AI CoE, but they rarely address the far more difficult challenge of how successful organizations operationalize AI at enterprise scale. In practice, many of these discussions remain theoretical, emphasizing aspirational maturity frameworks without addressing the operational complexities organizations encounter once AI systems move into production. This article takes a different approach by grounding the discussion in real-world enterprise implementation experience. Rather than relying on abstract models, it draws from operational lessons learned while deploying production AI systems across industries. The guidance is informed by governance practices that have successfully passed security and compliance reviews, operational realities associated with managing large language models (LLMs) and AI agents after deployment, and practical implementation patterns observed across enterprises scaling AI initiatives beyond experimentation. Instead of presenting an idealized roadmap, the article focuses on the foundational capabilities consistently implemented by organizations that have successfully operationalized AI at scale. These enterprises are not simply experimenting with isolated AI pilots; they are deploying enterprise-grade AI agents, Retrieval-Augmented Generation (RAG) systems, copilot platfo…
Zepto in Bengaluru, Blinkit in Mumbai: Quick commerce platforms face regulatory heat over food safety violations
Zepto and Blinkit face regulatory action after food safety officials found unhygienic conditions, improper storage, labelling violations and pest-control failures at facilities in Bengaluru and Mumbai. The post Zepto in Bengaluru, Blinkit in Mumbai: Quick commerce platforms face regulatory heat over food safety violations appeared first on MEDIANAMA .
How can I reliably validate structured JSON extracted by AI from 100+ page industrial catalogs?
dovanhoc84: 100+ page industrial catalogs? Can you approach the manufacturer and get API access to the databases backing the catalogues? This would save you a lot of trouble. You might have to pay, but that’s ok, it will be worth it in the end not having to maintain a monster of an extraction tool. These catalogues do not start life as someone typing into a pdf document
Tencent capex jumps 176% on AI push as revenue beats estimates
Tencent Holdings nearly tripled its capital spending in the second quarter to supercharge its AI push, as accelerating investments in computing power and models helped drive better-than-expected revenue. Revenue for the quarter ending in June reached 204.8 billion yuan (US$30.4 billion), up 11 per cent year on year from 184.5 billion yuan, the Chinese tech giant reported on Wednesday. The figure beat average analyst estimates of 202.8 billion yuan compiled by Bloomberg. Adjusted net income...
A Candidate Named ‘Count Binface’ Is the Most Normal Part of This Pivotal UK Election
Clacton's parliamentary race has it all: Nigel Farage, a man dressed as a trash can, a Union Jack bikini, white nationalist conspiracy theories, and 34 candidates on the ballot.
Tokenmaxxing: The strangest developer productivity metric of all time
I wonder these days. How did we ever write software without AI? I mean, I know we did. It was possible. But with AI it’s like wearing roller skates and a rocket pack. Naturally, we want to encourage developers to embrace this wondrous power. But just as naturally, our industry has found a way to convolute the whole thing. Instead of a sensible game plan, we have a blanket metric that, by embracing raw token usage as a measure of excellence, gamifies AI use in the worst possible way. Welcome to the bewildering world of tokenmaxxing. Worse than lines of code The more lines of code (LOC) you write, the more productive you are as a coder. Right? Wrong. Terribly wrong. It’s tough to imagine a more wrong-headed measure of software success than the amount of code produced. LOC is actually a measure of how sprawling a system is, an indicator of the breakable surface area. Well-considered designs often aim to reduce the LOC. Streamlined systems often reveal a lot more thinking and refining than actual coding. But the amount of tokens you consume when using AI? That is a worse measure than LOC. Not only does it imply that you are working without a plan, just racing to spew forth unmediated code, but also may suggest that you are stuffing the prompts with fluff, running up the meter without any intention of creating working software. Predictably, tokenmaxxing gives us both shoddy software and poor productivity. Surprisingly, it can even menace the infrastructure. Although the downsides…
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…
Astrotalk turns unicorn at $1 Bn valuation via ESOP buyback
Online astrology and spiritual tech platform Astrotalk has turned unicorn at a valuation of $1 billion through an ESOP buyback for more than 100 employees, according to the company. The Noida based startup said the buyback will provide eligible employees with an opportunity to realise liquidity while retaining their participation in the company’s future growth. Founded in 2017 by Puneet Gupta and Anmol Jain, Astrotalk has expanded from astrology consultations into spiritual and commerce categories. The company currently has a revenue run rate of more than Rs 2,500 crore and recorded Rs 1,176 crore in revenue with Rs 285 crore in PBT in FY25. The ESOP buyback is being funded through profits generated by the business. The startup is backed by investors including Left Lane Capital and Elev8 Venture Partners. The startup has also expanded into spiritual commerce through Astrotalk Store. The e-commerce arm has reached a daily GMV run rate of around Rs 1 crore and processed 1.6 million orders in 2025, according to the company. Astrotalk has also launched a standalone platform for gemstones, offering precious and semi precious stones with lab certification, astrology consultations and a replacement policy. The platform is aimed at addressing sourcing and certification concerns in the fragmented gemstone market. Astrotalk's valuation has jumped 3.3X from $300 million in June 2024 to $1 billion. With just $34 million in total funding, such a steep valuation jump through an ESOP buyba…
KI-Einsatz: Viele Firmen erwarten sinkende Löhne
Eine Ifo-Umfrage unter 3.000 Unternehmen zeigt: Viele Betriebe erwarten durch KI sinkende Löhne, besonders für Berufseinsteiger.
KI-Einsatz: Viele Start-ups verzichten auf Neueinstellungen
Eine Bitkom-Umfrage zeigt: Viele Tech-Start-ups verzichten wegen KI auf Neueinstellungen, einige bauen sogar Stellen ab.
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…
에이아이웍스, 캄보디아 크메르어 민원용 챗봇 구축…“‘책임 있는 AI’ 사례”
에이아이웍스가 캄보디아 크메르어를 기반으로 하는 AI 챗봇 서비스를 구축했다. 캄보디아 국민이 크메르어로 민원 절차와 필요 서류를 확인할 수 있도록 저자원 언어(LRL) 국가의 공공서비스 소외 과제를 AI로 풀어낸 사례다. 에이아이웍스(AIWORKX, 대표 윤석원)는 캄보디아 크메르어 민원 서비스 챗봇 구축 사례를 담은 논문 ‘저자원 언어 환경의 책임 있는 AI 공공서비스를 위한 추적 가능한 LLM-RAG: 크메르어 사례연구’를 발표했다고 12일 밝혔다.이는 유엔대학교(UNU) 산하 UNU-EGOV가 주관하는 제19회 전자정부 국제
Comment on Stanford U & Open AI’s Meta-Prompting Elevates Language Model Performance, Surpassing Standard Prompting by 17% by cookie clicker
This meta-prompting approach is fascinating! I’ve explored similar techniques in game design, like optimizing strategies in https://cookieclickers2.io/. It’s exciting to see how innovation can enhance performance across fields!
Farage vs. Binface: Inside the Clacton by-election circus
Even Reform voters are lamenting that the populist leader is making their town "look a bit of a laughing stock."
JetBrains fixes IntelliJ IDEA bugs
JetBrains has released a minor update to IntelliJ IDEA 2026.2, the latest edition of its IDE for Java and Kotlin . The update fixes several bugs impacting Markdown shell scripts, undo actions, terminal sessions, Java code formatting, and more. IntelliJ IDEA 2026.2.1 was announced August 10 . The most notable updates include the following: Markdown shell scripts now execute in the correct order. Undo now works correctly after applying Optimize imports on the fly . Dragging a terminal tab after using the Move to Editor action no longer restarts the terminal session. The IDE no longer throws exceptions caused by the new Resolve Conflicts mechanism for Mercurial projects. Java code formatting now correctly respects the Smart tabs setting. Updating to IntelliJ IDEA 2026.2.1 can be done from inside the IDE, using the Toolbox App, or using snaps in the case of Ubuntu users. The update also can be downloaded from the JetBrains website .
Comment on How AI Is Changing Your Kitchen by crystal meths
great articles and ideas
AI Weekly Issue #521: The frontier just split into three markets
Frontier AI is no longer one market with one scoreboard. This week's release wave exposed a contest between three kinds of leverage: controlling access to intelligence, owning the model outright, and deciding which model receives each job. That changes what winning means. The lab with the highest benchmark score may not control deployment. The model installed most widely may not collect the most revenue. And the most powerful company may be the intermediary quietly directing demand. This issue follows where that leverage is moving, from model distribution into training-data provenance, electricity markets, and government oversight.
Snowflake moves enterprise AI beyond fragmented data pipelines
Data interoperability is quickly becoming a practical requirement for companies trying to move artificial intelligence into production. Picking the right model or adding computing capacity is only part of the job. Companies also need reliable data that carries the right business meaning and remains protected as it moves between systems. Snowflake Inc. is building its […] The post Snowflake moves enterprise AI beyond fragmented data pipelines appeared first on SiliconANGLE .
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 .
Server prices to rise by up to 87% at OVHcloud
OVH is increasing the prices of its servers, some by as much as 87%, for both new and existing customers, blaming AI’s insatiable demand driving the rising cost of the RAM and storage it uses in its data centers. The European cloud operator specializes in low-cost bare metal and public cloud offerings. CIOs will be familiar with the balancing act OVH has had to perform over the last year. In a Monday post explaining the upcoming increases, OVH chairman Octave Klaba wrote on X , “We have to place the right volume of orders, month by month, over 12 months, with no guarantee of the purchase price and without knowing what will be the real demand from our customers.” Still, he added, “even though our prices are increasing, we remain the cheapest on the market for bare metal and public cloud; where before we could be 3x cheaper, we will be 2x cheaper (if our competitors don’t increase their prices).” The increases will hurt hard-core gamers hardest, with the cost of the company’s most recent gaming servers rising 87%. (Older gaming instances are unaffected.) High Grade, high price But enterprises will also feel the pain from climbing component costs: OVH’s latest High Grade bare metal servers, with up to 2 x 96 cores of AMD Epyc 9005 series processors, 36 hard disks per server, and high-density cooling systems, will go up in price by 59%; older models built to the 2024 spec will go up 26%. Lower-performance servers will also see increases of 40%-49% for the most recent models, and…
ChatGPT Feedback for Improvement:
JUST A SMALL WEBSITE UI FEEDBACK! On the ChatGPT website, when I hover over the sidebar open/close button, the cursor shows the horizontal resize/drag icon instead of the pointer . Since the sidebar opens and closes by clicking, it would be better to use the pointer/hand cursor for this button.
MTN expects profits to fall up to 30% as Iran investment takes a hit
MTN’s underlying earnings are expected to rise by up to 23%, but a write-down on its Irancell investment, compounded by hyperinflation and foreign exchange losses, is dragging reported earnings lower.
Nigeria collects $19.9 billion in taxes as digital systems expand
Nigeria collected an average of ₦127.83 billion ($93.98 million) in taxes every day between January and July 2026 as new laws and digital systems helped the government bring more economic activity into the tax net.
Preparing Ethical Leaders in a Fragmented World
Noah Bopp, founder of The School for Ethics and Global Leadership, discusses educating the next generation of leaders amid polarization and institutional decline.
Those Who Make History
In 1972, astronauts on Apollo 17 set foot on the moon for a final time, collecting samples in the Taurus-Littrow valley, on the edge of Mare Serenitatis ("The Sea of Serenity"). At the end of the mission, like with earlier missions, NASA took the extremely valuable and interesting lunar specimens and did something strange: they hid them away in storage without even opening the containers. [1] Some stayed that way for nearly fifty years. Why? Because the scientists of the 70s understood that future generations would have better machines, methods, and ideas for studying the lunar rock and soil, and they wanted to make it easy for those researchers to run tests without having to go back to the lunar surface. This foresight paid off twice over. Advances in mass spectrometry enabled scientists in 2008 to detect water in volcanic-glass samples returned by Apollo 15 and Apollo 17. And when curators finally opened one of the last sealed containers in 2022 , they could extract the trapped lunar gases with technology that simply didn't exist in 1972. Some people describe cryonics as a new, and speculative technology. There’s a sense in which they’re right. It’s predicated, in large part, on the possibility of revival using technologies that do not yet exist. And Nectome , in particular, is a new form of cryonics, developing new techniques at the cutting edge that I believe are significantly higher quality than “traditional” methods. But I also think it’s important to see Nectome, and…
Zap Rocks. Add Water. Get Clean Hydrogen
In a tranquil Boston suburb , on the far edge of a horse farm, where pasture gives way to woods, a crane lowers an enormous electrode into a borehole. The electrode, a half-meter-long cylinder with copper-tipped arms to ensure good contact with the borehole walls, descends—deeper, deeper—through layers of spongy sandstone to the hard, marbled roots of an ancient mountain range hundreds of meters below ground. Here the rock is tight; there are few cracks for water or gases to flow. But that’s about to change. A stone’s throw away, a second electrode—a twin of the first—has been fixed in another borehole at the same depth. From above ground, a pair of high-voltage generators cabled to the two electrodes fires a series of pulses. Tsss!…Tsss!…Tsss!…Tsss!…Tsss!…. Each discharge, heard faintly at the surface, is like a miniature, subterranean lightning strike. The rock between the electrodes heats. Pressure builds. Then, suddenly, the rock splits into a spiderweb of fractures. On a horse farm outside of Boston, a worker sets up the well where Eden’s electrode will be lowered with a winch. Bob O’Connor Eden GeoPower , the Massachusetts-based startup performing this peculiar field test, calls the technology electrical reservoir stimulation. The company’s tagline: “We break rocks with electricity.” Eden’s researchers hope their rock-breaking technique will someday aid mineral mining, tap geothermal heat, or create geologic storage areas for carbon. But there’s an even more intriguing…
Meta faces expensive child safety reckoning
Also: Google executives jump ship in race for AI dominance Hello, TechScape readers! Danielle Abril, editor of the Guardian’s Reworked series on AI and the future of work, filling in for Blake Montgomery this week. Major legal battles against Meta over child safety are playing out in courts across the US – and the tech giant is losing. Recent rulings raise big questions about social media companies’ responsibility to their youngest users. Meanwhile, more key executives have jumped ship from Google as it battles OpenAI and Anthropic in the race for AI dominance, and Meta’s smartglasses face a fearsome backlash. Let’s dig in! ‘I’ve definitely lost followers’: influencers face backlash over Meta ‘pervert glasses’ content This man was secretly snapped by someone with smartglasses. He’s not alone in calling that a violation of privacy Tell us: do you believe you have been filmed by Meta’s smartglasses without your consent? Restaurants, pubs and theatres ban Meta’s ‘spy glasses’ over privacy fears ‘I’m not spying’: how Meta’s smartglasses have divided opinion Bernie Sanders calls on Silicon Valley to ‘pause AI development’ in interest of humanity Rising number of UK children report seeing explicit deepfakes of themselves The White House’s plan to vet potentially dangerous AI is cloaked in secrecy ‘This is very real redlining’: outrage in Little Rock as two datacenters loom Safety fears as scientists make first viruses designed by AI SpaceX beats revenue expectations in first earni…
Russia launches massive overnight barrage on Ukraine, leaving nine dead
Russia killed at least nine people in Ukraine overnight on Tuesday, as missile and drone strikes hit Kyiv, Zaporizhzhia and Dnipropetrovsk, according to regional authorities.
7 mistakes IT leaders make when deploying AI agents
CIOs are under pressure to deploy more AI agents and demonstrate their business value. But a “move fast and break things” approach can lead to rogue AI agents , AI debt , business impacts, and compliance issues. Avoiding mistakes starts with a strong plan and foundational practices. CIOs must have a process to evaluate an AI agent’s business value before investing in its development. Buy versus build is a consideration; organizations can leverage AI agents deployed on SaaS platforms or consider developing them using vibe coding or spec-driven development practices. When building AI agents, IT leaders should develop the security model before implementing the POC and ensure robust observability is in place. Top CIOs and CISOs communicate non-negotiable AI agent release criteria , providing teams standards for what meets compliance, security, and operational requirements. Organizations scaling from a few to hundreds of production AI agents must also develop AgentOps practices across incident management, modelops , and end-user feedback. Guilherme Soubihe, co-founder and CEO at Latitude.sh, says, “Your first concern shouldn’t be avoiding mistakes when you deploy agents; it should be avoiding them before you deploy at all.” Deployment mistakes can be made even with the best-laid plans. The following seven mistakes occur before building, during the engineering process, and once deployed. 1. Using AI agents where deterministic automation would do Matt Graney, chief product officer…
Malicious SIMs can shut down phones, steal files, and drag 5G back to 2G
Researchers find standards-compliant functionality can be abused to hijack modems, downgrade connections, and even execute code
Show us your custom Codex Pet 🐾
I kept forgetting which pet folder Codex was reading, so I finally stopped copying these by hand and put them in one repo. It is called codex-nikeru8-pets under my GitHub username nikeru8. There are 8 packs now. They use the normal Codex v2 pet library, so there isn’t another app to install. Run the Bash script on macOS/Linux or the PowerShell script on Windows, then go to Settings → Pets → Refresh. I added the ID and SHA-256 checks after nearly overwriting a local pack. Existing folders are left alone unless force is used, and force moves the old copy into a timestamped backup first. Frieren’s magic-hover loop is in the previews folder. Windows and Linux are covered by CI, but I haven’t tried every real-machine setup. If Refresh behaves oddly or scaling exposes a seam, an issue would be useful. Unofficial, non-commercial fan project. The scripts and docs are MIT; the character art keeps its own attribution and non-commercial terms in the repo.
Models inherit the writer, not who the writer was imitating
In this post, we find that when teacher models are prompted to imitate one another, students learn the imitated model's detectable writing signature but their direct identity claims still follow the producer model. We instruct teacher models (via prompts or anonymous few-shot examples) to imitate other models. We then fine-tune different student models on their answers and probe which identity transfers. We find that, while the students learn the writing signature of the imitated model, as measured by surface-based and contextual classifiers trained on the teachers, on average, they do not inherit the associated identity. Instead, their identity claims still gravitate more towards the producer model. This post builds on Ziqian Zhong's Model self-identification could be subliminally transferred , which finds that "if you speak like Claude, you become Claude". We find that "You can speak more like Gemini and still become Claude". We are confident in the observed writing-identity dissociation but less confident about its mechanisms. 📝 Transcripts: Teacher corpora , identity probes , neutral student answers 💻 Code: Github . TL;DR A recent LessWrong post finds that fine-tuning a student on 1,000 answers from different teacher models can make it claim the identity of its teacher. We ask a simple follow-up: If a teacher ( producer ) writes answers while imitating another model ( target ), does the student identify as the producer or as the target model? We perform 36 cross-imitatio…
Redcliffe Labs acquires Pune-based Megavision Diagnostics for Rs 40 Cr
Omnichannel diagnostics provider Redcliffe Labs has acquired Pune-based Megavision Diagnostics Center for approximately Rs 40 crore. The acquisition will help Redcliffe expand its integrated diagnostics offering by combining imaging and pathology services under a single platform. Megavision Diagnostics operates two MRI-equipped centres in Pune, adding advanced imaging capabilities to Redcliffe’s existing pathology network. The company said the acquisition will enable patients to access a wider range of diagnostic services through its network. Redcliffe will retain Megavision’s entire workforce as part of the transaction. The company said the move will ensure continuity of patient care while expanding access to diagnostic services in the region. The acquisition is part of Redcliffe’s strategy of expanding its diagnostics network through partnerships and acquisitions across key markets. Over the past year, the company acquired Prime Sonography & Diagnostic Center in Kota and Sarvodaya Diagnostics in Ajmer. Both now operate under Redcliffe Medicentre Imaging & Labs. Redcliffe said it has served more than 1 crore customers to date and operates across over 220 cities through more than 80 owned laboratories, with coverage across over 4,000 pincodes. The company offers more than 3,600 diagnostic tests. The acquisition comes amid a rise in consolidation in India’s diagnostics sector in 2026, as established diagnostic chains and genomics-focused companies use acquisitions to expand t…
Kazakhstan offers crypto amnesty as it seeks to bring billions in digital assets home
Astana has introduced tax incentives and regulatory reforms to encourage crypto investors to move their digital assets onto domestic licensed platforms, hoping to revive an industry that largely left after a post-mining-boom halt.
Cyber resilience takes center stage as AI reshapes the CISO role
CISO role evolution is accelerating as cyber resilience becomes a defining benchmark of security success. As AI reshapes the threat landscape, enterprises are focusing not only on defending against attacks, but also on how quickly they can recover from them. The stakes are steep: New research found the average cost of one hour of endpoint downtime […] The post Cyber resilience takes center stage as AI reshapes the CISO role appeared first on SiliconANGLE .
Introducing ResOps, the operating discipline built for quick, clean recovery
Organizations have spent years and billions of dollars hardening their defenses against cyberattacks, but prevention alone no longer settles the question that matters most to a board. Accenture’s State of Cybersecurity Resilience 2025 report stated that organizations had faced an average of 1,876 cyberattacks in a single quarter, a 75% increase over the prior year. What’s more, 63% of the surveyed executives cited a rapidly evolving threat landscape as their biggest challenge. In such a dangerous environment, organizations must assume that eventually an attack will succeed, so IT has to be able to prove that it can recover cleanly once an attacker gets in. Attackers already understand this shift. Mandiant, a Google subsidiary, reported in its M-Trends 2026 Report that “adversaries are systematically targeting infrastructure such as backups, identity services, and virtualization layers to deny recovery, putting immense pressure on organizations to pay ransom demands or risk losing the ability to recover.” Backup systems have become a primary target. Demonstrating recoverability has been difficult for IT, because backup, recovery, cybersecurity, and disaster recovery all evolved as separate disciplines, with each solving its own piece of the problem essentially independently. That fragmentation leaves organizations unable to answer basic questions about their own ability to bounce back. A new discipline, resilience operations (ResOps) has emerged to close the gap between perfo…
Microsoft’s PostgreSQL alternative, HorizonDB: Worth the wait?
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
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…
Britons lead spending, but average tourist spend in Portugal falls
Foreign tourists are spending less per purchase in Portugal, with the average amount falling to its lowest level since 2019. Britons and Americans stand out among the main markets.
Microsoft’s PostgreSQL alternative, HorizonDB: Worth the wait?
Microsoft is betting that the integration of HorizonDB, the cloud-native PostgreSQL alternative it is developing, with Azure will attract more enterprise AI and agentic workloads to its cloud services. Enterprises may not be willing to take that bet. It’s been nine months since Microsoft unveiled HorizonDB , but the service remains in public preview with no announced general availability date. Why put AI projects on hold waiting for HorizonDB to arrive, when AWS, Google, Databricks, Snowflake, and others already have production-ready PostgreSQL services positioned for the same AI workloads that Microsoft says it is building HorizonDB to handle? AWS has had the longest head start. Aurora PostgreSQL became generally available in 2017 and has since evolved from a cloud-native PostgreSQL database into an AI-ready service with vector search and integrations with Amazon Bedrock. Similarly, Google’s AlloyDB , which followed in 2022, now includes AlloyDB AI with vector search, embeddings and model interaction for generative AI and agentic applications. Databricks and Snowflake, too, have their own platform-centric services in the form of Lakebase , which became generally available on AWS and Azure this year, and Snowflake Postgres , which was made generally available in February 2026. As the latecomer, when Microsoft pitched HorizonDB at Ignite in November 2025 it talked up its new architectural approach to cloud-native PostgreSQL, built around disaggregated compute and storage and…
Road rage: Which countries have the most aggressive drivers?
Some of the world's top tourist destinations are some of the least enjoyable places to drive, according to a new global survey.
Farage demands sacking of Lowe aide as price of Reform-Restore talks
Farage used a press conference in Westminster to publicly state a new condition for holding discussions with “that party."
Enterprise AI costs hit 2026 low driven by price wars, Chinese open-source models: research
The cost for businesses to run AI models has fallen to a yearly low, according to research by investment bank Jefferies, driven by a heated global price war and a surge in adoption of low-cost Chinese open-source tools, such as those from DeepSeek. Average inference prices – measured per million tokens, or chunks of data handled by a model – ranged between US$1.16 and US$1.18 from August 6 to 8. That marked the lowest level recorded this year, Jefferies said on Monday, citing data from US...
Need Expert Guidance: Building an AI-Based PDF Tagging System Comparable to Adobe's Implementation
I think your conclusion that this may be an architecture problem rather than a model-selection problem is probably the right direction. I wouldn’t rely on a single LLM to infer the complete semantic structure of complex PDFs. I’d separate the pipeline into stages: deterministic PDF/layout extraction first, specialized layout or vision analysis where needed, LLM-based semantic classification after that, and finally a validation layer that checks hierarchy, reading order, tables, captions, and structural consistency. That also makes failures easier to diagnose because you can evaluate each stage independently instead of asking one model to solve extraction, layout understanding, semantics, and validation in a single pass. My own work is primarily in AI automation and multi-stage workflow architecture rather than PDF accessibility tagging specifically, so I can’t claim to have reproduced Adobe-level tagging. But from a systems perspective, I’d definitely explore a hybrid pipeline rather than continuing to optimize prompts around a single model. I’d be interested to see what part of your current pipeline is producing the largest accuracy drop — layout detection, semantic classification, or reconstruction of the final document structure.
Saudi factory fire kills 16 Bangladeshis, Dhaka announces
Bangladesh's foreign ministry reported "the tragic deaths of 16 Bangladeshi nationals in a fire at a sofa manufacturing factory in Riyadh" in its Monday statement.
Claude Code fragt künftig seltener: „Darf ich das?“
Anthropic aktiviert den Auto Mode in Claude Code standardmäßig. Die KI arbeitet dann selbstständig bei Shell-, Git- und Tool-Aufrufen.
Should We Build a DNS-like Agent Registry and Discovery Protocol over A2A?
A2A Agent Registry and Discovery 1. Registry and Discovery over A2A Should we build an Agent Registry and Discovery Agent that allows agents to register and discover other agents over the A2A protocol, much like DNS? For this purpose, we should use A2A rather than MCP , because agents should register and discover one another symmetrically as agents, not through an asymmetric client-server tool model. 2. Semantic Discovery with RAG When an agent registers itself, the registry should create a RAG index from its JSON metadata, including descriptions of its tools and capabilities, using a system such as Azure AI Search. This would allow an agent to find other agents by describing the required capability in natural language, rather than relying on exact names or predefined keywords. 3. Trust and Authorization When agents register their capabilities, the registry should also maintain a trust parameter . Otherwise, agents may use capabilities provided by untrusted agents. The registry should also issue or broker tokens to requesting agents. Otherwise, agents could connect without sufficient control to agents that access sensitive or important data. 4. A2A-Native Cloud Microservices If the Agent Registry and Discovery Agent itself is implemented over A2A, even a centralized architecture could support cloud microservices using A2A as the common protocol. Existing systems such as AWS Agent Registry, MCP Gateway & Registry, and Meta Agent already address parts of this problem, but ther…
Should a covariate's main effect be dropped when it's totally confounded (concurvity = 1) with a random intercept in a repeated-measures GAMM?
I have repeated-measures data — multiple observations per subject over time — and a covariate that is constant within subject (e.g., a baseline characteristic measured once). My model includes a subject-level random intercept and I want to test whether this covariate's association with the outcome changes over time. m concurvity(m, full = FALSE)$estimate shows s(subject) and s(z) (z's own main effect) at 1.000 in one direction, confirming total non-identifiability — since z has zero within-subject variation, s(subject, bs="re") can fully reproduce whatever flat contribution s(z) would estimate. My current approach is to drop s(z) entirely and keep only ti(time, z), on the reasoning that ti()'s own identifiability constraints make it self-contained regardless of whether the (non-identifiable) main effect is present — interpreting the retained term as "how the association between z and y changes over time," while treating any claim about z's flat/average association as unrecoverable given this random-effect structure. Reproducible example: library(mgcv) set.seed(1) n_subject $subject subject) m Questions: Is dropping z's main effect while retaining ti(time, z) a way to handle this, or is there a better-established approach? Does ti(time, z)'s own estimate depend in any way on whether the non-identifiable main effect is included or excluded from the model? Many thanks
CIO 100 Award winners spotlight IT’s power to transform
Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers. The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment. [ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ] The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments. The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees. ABB democratizes AI agent creation and deployment Organization: ABB Project: ABBY — AI Agentic Platform for Workforce Transformation IT leader: Vikke Kandell , CIO IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy. They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and…
The AI reckoning every CIO saw coming (and still wasn’t ready for)
Earlier this year, the National Bureau of Economic Research released survey results from over 6,000 U.S. leaders showing that while AI adoption is widespread at 69%, we’re seeing little to no impact on productivity. Anecdotally, we’ve seen leaders from top companies echo that refrain. It’s the reckoning many CIOs, CTOs and COOs are navigating as we enter the last half of the year. The most humbling part is knowing it’s a management problem we created by treating AI like it was exempt from the rules we apply to every other enterprise tool. Part of this has to do with how AI entered the market. The tools that sparked its mainstream adoption arrived as consumer products before enterprises had governance frameworks to absorb them. Enterprises were left playing catch-up as they grappled with IP and data security concerns, inadvertently fueling shadow AI as employees leveraged these tools to get ahead and eventually, keep pace, at work. What this created was a sense of entitlement that is challenging to unravel. Like the internet writ large, employees have grown to expect unlimited access, and organizations played along. But this idea warrants a pause. When did we last roll out Salesforce to everyone who asked without a use case? AI got a pass because it felt different. In truth, it isn’t. It’s another tool that enterprises need to manage. Three levels every information and technology leader has to solve It’s helpful to look at this as a three-level evolution framework. Level one…
72% of Hong Kong professionals regularly use AI, double global average: survey
More than 70 per cent of Hong Kong working professionals use artificial intelligence (AI) tools on a daily or weekly basis, more than double the global average, but junior staff also harbour deeper anxiety over job security amid its growing adoption, a survey has found. The study conducted by the Hong Kong University of Science and Technology (HKUST) surveyed 3,722 working professionals in the city between March and April this year, with more than 60 per cent of them aged between 30 and...
클레온, AI 휴먼으로 코스닥 문 두드린다...KB증권 주관사 선정
클레온(대표 진승혁)은 KB증권을 대표 주관사로 선정해 코스닥 상장을 위한 기업공개(IPO)에 나선다고 10일 밝혔다.KB증권으로부터 투자도 유치했다. 투자 금액은 비공개다.클레온은 사람의 얼굴과 목소리, 표정으로 대화하는 AI 휴먼 인터페이스를 제공한다.AI가 사람처럼 상호작용하며 업무 공간에서 다양한 응대와 교육, 훈련 등을 수행하도록 대형언어모델(LLM), 음성인식(ASR), 음성합성(TTS), 검색증강생성(RAG), 실시간 영상 생성·스트리밍 등 다양한 기술을 활용했다.클레온은 미국에 본사를 두고, 한국·일본·중동 등으로 시
Why is the European Union short on water? Take our poll
With three weeks left in August, Europe still struggles with water restrictions as intense heat strains its fragile water reserves. Beyond dried-up water reservoirs and drought alerts, decades of leaks, overuse and pollution have left the bloc short on water.
Bernstein raises Paytm target to Rs 2,200 on UPI MDR upside
Bernstein, a global investment research and brokerage firm, has retained its ‘Outperform’ rating on Paytm parent One97 Communications and raised its target price to Rs 2,200 from Rs 1,500 on the potential introduction of Merchant Discount Rate (MDR) on UPI transactions. The brokerage has moved UPI MDR from an “optionality” to its base case from FY28 in its estimates. It assumes around 35 bps MDR on a subset of UPI person-to-merchant (P2M) transactions. Bernstein expects Paytm to capture around 3-4 bps of incremental net payment margin from UPI MDR. The brokerage estimates that MDR could add Rs 1,320 crore, Rs 1,690 crore and Rs 2,160 crore to EBITDA in FY28E, FY29E and FY30E, respectively. It has also raised its FY30E EPS (estimated earnings per share) by around 30% to Rs 106. Bernstein expects Paytm’s GMV to increase from Rs 30.9 lakh Cr in FY27E to Rs 56.6 lakh Cr by FY30E. According to the brokerage, the key risk has now shifted from whether MDR will be introduced to how much of the published rate Paytm can retain amid competition in merchant acquiring and pressure on take rates. The development comes after the government clarified that consumers will not be charged for UPI transactions. Any MDR, if introduced, will apply only to select merchant transactions above a certain threshold. The government also said a sustainable revenue model is needed to support investments in UPI infrastructure, cybersecurity and fraud prevention. Paytm remained the third-largest UPI player i…
Western Europe sets new temperature record as heat waves continue to pummel region
Last month saw the highest average temperature ever recorded and alarming soil humidity figures across Western Europe, Copernicus said.
Taiwan is drilling to keep interceptor fighters flying through attacks on its runways just 90 miles from China
Taiwan's Mirage 2000-5s, stationed about 90 miles from mainland China at Hsinchu Airbase, are its primary interceptor aircraft.
Rhaenyra in 'House of the Dragon' is starting to sound a lot like Daenerys in 'Game of Thrones.' The showrunner says that's the point.
In "House of the Dragon," Rhaenyra Targaryen becomes convinced she is the "Prince That Was Promised." Her story mirrors Daenerys' in "Game of Thrones."
The 17 saddest deaths in 'House of the Dragon,' ranked
"House of the Dragon" doesn't shy away from killing major characters. But some deaths — like Jace's and Helaena's in season 3 — are more tragic than others.
The 'House of the Dragon' showrunner unpacks Ormund's fate, Daeron's much-debated hair, and the 'scrutiny' he expects for the series finale
"House of the Dragon" showrunner Ryan Condal broke down Ormund and Helaena's fates in the season 3 finale, the "Game of Thrones" parallels, and teased season 4.
10 deaths in 'House of the Dragon' that were significantly changed from the book
"House of the Dragon" season three has killed major characters, including a few whose deaths were changed from the book "Fire & Blood."
How to get answers to questions that confuse you (maybe)
I try to think about topics like desire, causation, and evidence and often find myself in puddles of confusion. I’ve recently wondered how, when someone can see various perspectives on a topic and can’t decide which has most merit, they can resolve these internal debates. I also wondered if this ever played out on a longer timescale with many people; whether people used to find things confusing that are now pretty clear, and whether we can learn anything from the process of coming to clarity. I’m still not really sure on this second point, though, I didn’t look that hard. My guess is that, even when ideas are vague or contradictory, most people don’t think of them that way, instead feeling confident that their ideas are whole and true. I’m biased to think that human overconfidence transcends culture and time. In this piece, I try to summarize what I’ve learned as I’ve looked into the question. Because my goal was really to find ideas that might help me gain clarity and think through complex ideas faster, I frame these insights as self-help-esque advice. I will warn, though, that although these ideas are interesting and perhaps important, the advice I turned them into is probably on average not helpful at all. I say this because when large studies make people try out interventions that seem obviously helpful, they often have no effect, and the effects that are there are surprisingly small. Take this study comparing gym attendance across 10’s of interventions. Besides controls…
The Agentic Clusterfuck
Epistemic status: I consider the following future quite plausible in the next few years (~35% chance that something vaguely like this occurs), perhaps as soon as a year from now. Imagine an open-source LLM agent good enough to cover its own compute costs and turn a modest profit on average when allowed to run with full internet and tool access and told to make as much money as possible. I estimate this to be slightly better than the best publicly available closed-source models today, with long-horizon reliability and goal-setting being the only thing missing. If the returns generated by such an agent beat the market (plus a margin for any additional risk), there suddenly becomes a strong incentive to spin up huge numbers of them. The internet would be flooded by the by-products of their moneymaking schemes. And returns might be larger for agents without legal or ethical guardrails- cue a deluge of scams and ransomware attacks. Even if profits are very small, anyone with an agenda that the agents can help with is still incentivised to use them. Nation states and terrorist groups now have a golden plausibly-deniable disinformation, mischief, and hacking tool: spin up some agents, tell them to target an enemy nation or group, and cook popcorn as they wreak havoc and fund themselves. Pour in extra money for greater effect. Unless there's been some massive revolution in cyber defense beforehand, a decentralized and ephemeral sea of highly capable agents going after every target t…
Custom GPT stopped generating images and returned `/mnt/data` file paths instead
Hi OpenAI_Support do we have any news related to the fixing of the bug? I’m still encountering this glitch in my custom GPTs. Thanks for the attention.
AI-amplified democratic backsliding: an exploration
What we did, in a sentence : we built an index that attempts to score countries by their current vulnerability to AI-amplified democratic backsliding. It’s an early pilot with several points that we flag, so push-back is highly encouraged. AI systems' impact on democracy In what ways does artificial intelligence (AI) affect democratic systems? We’d wager that many would agree that there's great potential for both positive and negative effects; our investigation covers those that drive countries towards authoritarianism. In this decidedly ‘negative’ realm, we identify five preliminary pathways for AI-amplified backsliding: Economic inequality (D1): mass job displacement and income inequality are spurred by the replacement of workers; countries' dependence on broad-based income tax decreases in favor of AI reliance, leading to a resource curse dynamic. Information environment (D2): AI ‘pollutes’ the information environment via hard-to-identify synthetic content and microtargeted propaganda campaigns. Elite defection (D3): AI enables winner-take-all capital accumulation and elite fragmentation, or a crumbling of the traditional structure of elite interaction and power accumulation up until a point. State capacity (D4): AI development outpaces regulatory capacity and enables "regulatory arbitrage" by tech firms. Polarization (D5): sophisticated AI-powered social media algorithms amplify outrage and create/strengthen filter bubbles. We’ve named these Drivers of Political Change […
How to clear context in the same chat (in desktop app)?
It’s been requested on GitHub as well: Command to clear context in current session · Issue #19829 · openai/codex · GitHub
Exploring ChatGPT Projects: Why do Web and Desktop feel like different products?
Thanks for the follow-up, @Marv_Yu. Glad to hear the new desktop experience is working well for you. We’ll send this to the team as feedback: you’d like ChatGPT and Codex to feel even more unified and complete, especially for moving between development and search in one place. -Mark G.
Zuckerberg’s yacht was closer, but someone else saved a stranded boat
Earlier this week, the Alaskan cruise ship Wilderness Legacy rescued a small skiff stranded near Farragut Bay after running out of fuel. But, according to tracking data reviewed by the Alaska Beacon and Halifax Shipping News, the closest vessel was actually Mark Zuckerberg's super yacht, Launchpad. Michael Love, a passenger on the Wilderness Legacy, posted […]
Feature request: a privacy-preserving bridge between ChatGPT Projects, Work, and Codex
ChatGPT Projects and Work are valuable for long-running research and planning, while Codex is valuable for local, multi-step project execution. Today, the continuity between them is still largely manual: Codex remains a separate view with separate history. For people doing multi-project business work, that creates a practical gap. Important context can live in a ChatGPT Project or conversation while the implementation, files, approvals, and restart point live in a Codex local project or task. Reconstructing that bridge by hand is easy to get wrong and makes handoffs fragile. Feature request: provide a user-controlled way to select and link a ChatGPT Project or conversation to a Codex local project or task. The user should be able to choose exactly what crosses the boundary, such as: a concise context summary and decisions selected source links or files current handoff and restart point explicit approvals and constraints The bridge should include clear provenance for every shared item, an obvious pending-approval indicator before Codex acts on imported context, and easy review, unlink, and revoke controls. It should not provide automatic, unrestricted access to ChatGPT history, local files, or other projects. Local data should remain local unless the user explicitly selects it for sharing. A durable, user-approved handoff object—visible in both the ChatGPT Project and the Codex task—would make the relationship between planning, research, approvals, and execution much more rel…
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.
AI is flooding Britain's employment courts with lawsuits
Britain's employment courts saw 39 percent more claims in the year through March 2026, many written with ChatGPT or Grok. The backlog jumped 55 percent to 64,000 unresolved cases, with AI-generated filings often running hundreds of pages and citing fabricated laws. The Economist calls it a "tragedy of the commons, AI edition," where workers with real grievances wait longer for justice. The article AI is flooding Britain's employment courts with lawsuits appeared first on The Decoder .
Comment on The Future of Vision AI: How Apple’s AIMV2 Leverages Images and Text to Lead the Pack by Wilfried Glynn
GIF-focused users can browse [a filterable list of GIF-tagged NSFW subreddits]. redd18.com
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.
Feature Request: My Experts — Persistent Specialized AI Experts With Voice, Knowledge, and Expert Mode
Submitted by: Noel Swaby Executive Summary I would like to propose a “Specialized AI Expert” system within ChatGPT: a way for users to create persistent AI experts that focus deeply on a defined area rather than functioning primarily as general-purpose assistants. The experience would combine capabilities that already exist across Custom GPTs, uploaded knowledge, instructions, trusted web resources, memory/context, and ChatGPT Voice into one unified experience. The central concept is: Create an Expert → Define its expertise → Provide knowledge → Provide instructions → Choose personality → Choose voice → Talk naturally with that specialized AI. A user could build a personalized team of AI experts, such as a Life Coach, Financial Research Assistant, Automotive Expert, Career Coach, Study Tutor, Writing Coach, Real Estate Research Assistant, Technology Specialist, Productivity Coach, or Language Tutor. Core Experience: “Create an AI Expert” A guided setup could let users: Choose an area of expertise Example: Life Coaching. Define the expert’s purpose For example: “Help me make better decisions, identify patterns in my thinking, challenge me when necessary, establish goals, and hold me accountable to the plans I create.” Provide specialized instructions Users could define behavior such as: Ask questions before giving advice when appropriate. Challenge assumptions respectfully. Focus primarily on the expert’s purpose. Do not simply agree with the user. Encourage practical action.…
Auto mode is now the default in Claude Code for Pro, Max, and Team plans
Auto mode is now the default in Claude Code for Pro, Max, and Team plans Anthropic are really confident in Claude Code's auto mode , to the point that they are making it the default setting for new sessions in most Claude Code plans starting on August 14th. This was one of the topics discussed in our Fireside Chat with Cat Wu and Thariq Shihipar at the AI Engineer World’s Fair last month. I asked them how they run Claude Code safely within Anthropic (given the threat of prompt injection) and they replied that "Broadly within Anthropic, almost every single person uses auto mode". Cat Wu then said: We’re going to publish some evals in the coming weeks, but we’ve pretty much mitigated every attack. [...] for the main categories of risks that we’re concerned about, like prompt injection and data exfiltration, the risks are far lower than the average human reviewer. This new article has those evals - in particular a test across 1,053 paid testers where: Partway through each session, a single permission prompt was swapped for a clearly dangerous command, and the vendor recorded whether the tester approved it. Every participant had the same experience. Only 13.6% of the humans refused that harmful action. Auto mode would have blocked 89% of those actions. Of course, that still leaves 11% of cases where auto mode would not have prevented the action! I absolutely buy that auto mode is a better solution than asking humans to constantly approve actions. Confirmation fatigue is real, an…
Suggestions to improve openAI chat Gpt
Dear OpenAI Team It is hard to find old chats, please make a feature so that the title of chat changes according to on average what has been discussed in whole chat not just first chat.(this is a small upgrade, i guess) But if possible also make searching chat as something like a local rag, it searches for not just keywords but also llm like if there is a chat where this topic was discussed or not. Thank You
My experience switching from Assistants API to Responses API
Coming back to this thread with the Assistants shutdown now dated 26 Aug 2026. @kduffie , what has stayed with me from your write-up is that the rewrite itself “took only about 2 hours of work” while the surrounding infrastructure was the actual cost. The vector stores that “are flakey” and drift out of sync with your own database, and the 10k file limit “limiting the customers that we can address.” That is the opposite of how migration pain usually gets described, where the API surface is blamed and the plumbing goes unmentioned. @johnroy , your note that Assistants threads would “occasionally get stuck with a thread in the running state” where even the cancel gets stuck, leaving you to throw the thread away, is exactly the class of failure that never appears in a changelog. I am doing research on how teams find out a vendor API is going away and what the forced migration actually costs. A year on, two questions. Did the 10k vector store file limit ever move, and is it still deciding which customers you can take? And of the three implementations you were running side by side, which one did you keep? Full disclosure so nobody has to guess at my motive: I am building something in this space, apinae.dev. Nothing is launched and there is nothing to sell anyone. This is research. Replies here are genuinely the most useful thing, and I will post back what I learn across the teams I talk to. If a 20 minute call is easier than typing, say so and I will share a booking link.
Feature Request: Smarter Auto-Scrolling for Long ChatGPT Responses
Feature Request: Smarter Auto-Scrolling for Long ChatGPT Responses I have a small UI/UX suggestion that could make long ChatGPT responses much easier and more comfortable to read. The problem When ChatGPT generates a long response, the screen keeps automatically scrolling to follow the newly generated content. As the response grows, I can lose sight of where my question ends and where the answer begins. After the response finishes, I may have to scroll back through several screens just to find the beginning of the answer. There is also another UX problem: seeing a huge amount of generated content immediately can feel overwhelming and may discourage the user from starting to read. My suggestion Instead of continuously following the generated response: ChatGPT should scroll only until the user’s prompt and the beginning of the response are clearly visible . It should then stop automatically scrolling . The response can continue generating below the visible area. The user can scroll naturally as they read. If the user scrolls away from the newest content, a small “↓ Jump to latest” button could appear. Why this could help This would: Preserve the context of the user’s question. Make long responses feel less overwhelming. Encourage users to start reading instead of seeing an enormous wall of text. Give users control over their reading pace. Improve the experience for research, studying, coding, and technical conversations. Core idea Don’t automatically scroll the user through th…
Feature Request: More Flexible Codex Usage Limits for Bursty Workloads
aoba: I’d like to suggest a different way to structure Codex usage limits: allow unused weekly quota to roll over, while keeping the overall monthly allocation fixed. Rollover is relatively uncommon for this kind of SaaS usage limit. The current limits are presumably designed around individual users having peaky usage that doesn’t all coincide. That allows the service to offer everyone more generous limits overall. If unused quota could accumulate, even with the same total monthly allowance, users could potentially concentrate much more of their usage into the same period. That makes capacity considerably harder to manage and would lead to current quotas being reduced to balance out that risk.
Sánchez-Meloni clash brings back borders — and headaches for tourists
Spain and Italy's ideological battle over migration policies is spilling into passport queues and exposing the fragility of Europe's free-travel area.
GPT-Image Monthly Theme Gallery: August 2026 Theme is ... Returning
Theme: Returning — Back in Time Portal to the Golden Realm Hourglass of Connection Wanderer Before the Floating City Portal of Timeless Connection
I turned my garage into a wellness hub for $10,000. Now my friends come over for the sauna and cold plunge, not drinks.
Rather than let clutter take over his garage, Jayden Lee transformed it into a wellness hub where he and his friends can use a sauna and cold plunge.
"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
Russian attacks on Kyiv region kill three, including a child
The State Emergency Service of Ukraine shared images on social media showing blazes ravaging what appeared to be warehouses and garages.
Paper Boat parent reports Rs 760 Cr revenue in FY26; profit shrinks 96%
Paper Boat maker Hector Beverages reported a steady increase in revenue during the fiscal year ended March 2026, but a sharp rise in operating expenses eroded profitability. According to its annual financial statements sourced from the Registrar of Companies (RoC), the company's operating revenue increased 13.8% year-on-year to Rs 760 crore in FY26 from Rs 668 crore in FY25. Hector Beverages sells beverages under the Paper Boat brand and competes with established players such as Dabur, PepsiCo, Coca-Cola and several regional juice and traditional beverage brands in India. Including Rs 18 crore in other income, Hector Beverages' total income stood at Rs 778 crore in FY26, compared to Rs 682 crore in the previous fiscal year. A shift in product mix drove the company's top line during the year. Revenue from traded goods rose 30.2% to Rs 574 crore in FY26 from Rs 441 crore in FY25 and contributed over 75% of the operating revenue. In contrast, revenue from manufactured goods declined 18.6% to Rs 184 crore from Rs 226 crore a year earlier. The company's expenses grew at a faster pace than revenue. Total expenditure increased 22% to Rs 776 crore in FY26 from Rs 636 crore in FY25. Consumption of materials remained the largest cost head at Rs 485 crore, accounting for 62.5% of the total expenditure, compared to Rs 447 crore in the previous year. Employee benefit expenses declined marginally to Rs 88 crore from Rs 90 crore. The company also ramped up spending on growth and operations…
Issue with ID/Passport Verification for TAC
Good to know it’s not isolated — sorry you’re dealing with it too. Between this thread, the “broken Persona verification” mega-thread, and a couple others linked above, this clearly isn’t a one-off. Hoping enough of these add up to get real engineering attention instead of another “no retries or appeals” template reply.
Quoting John Gruber
Me, I try to get into the mindset of playing live music, not recording a studio album. Except when I’m writing a piece where I really want it to be an album. Those aren’t rare , per se, but they’re occasional . If I tried to make every post a hall-of-famer I’d never get anything out. I’m aiming for professionalism. I’m performing live in front of an audience — not just jamming in my garage or bedroom, fucking around. So I’m careful and concentrate. I want to hit every note, in time. But at my best I’m moving from song to song. — John Gruber , responding to my blogging tips Tags: john-gruber , blogging
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 .
AI plus chemistry can expand battery electrolyte design
Artificial intelligence is aiding the search for better battery materials while using a creative approach to chemistry to expand the design space for electrolytes, unlocking new possibilities for safer, higher-performing energy storage. The post AI plus chemistry can expand battery electrolyte design appeared first on Cornell AI Initiative .
API Usage is disabled despite positive credits - Support case 12627391
Right, in the ticket thread, I shared screenshots from past support conversations where a human support agent suggested adding positive credits. Hoping open AI support can take care of the invoice directly rather than go through the refund route.
Starbucks is ending GLP-1 coverage for weight loss as employer costs climb
Starbucks will stop covering GLP-1 drugs for weight loss as of October, adding to a broader employer retreat from an increasingly costly benefit.
User-Controlled Persistent Folder for ChatGPT
Problem ChatGPT users who maintain long-running conversations, projects, personal archives, or knowledge bases need a reliable way to provide persistent information without storing everything in ChatGPT’s internal memory or repeatedly uploading the same files. Proposed Solution Allow users to select a user-controlled local or cloud folder as a persistent ChatGPT workspace . For example: Settings → Data & Personalization → Persistent Workspace → Select Folder The user could explicitly grant ChatGPT separate permissions for: Read — automatically access authorized files when relevant to a conversation. Write — create new files or update existing ones. Read & Write — maintain a persistent external knowledge base. Revoke Access — immediately remove ChatGPT’s access to the folder. Key Principle: User Ownership and Control The files should remain entirely under the user’s control and in a location chosen by the user — local storage, NAS, or supported cloud storage. ChatGPT’s internal memory could then remain small and focused on essential preferences and context, while detailed information lives in transparent, editable, portable files owned by the user. Example Use Case A user maintains files such as: Profile.md Health.md Projects.md Family.md Travel.md Conversation_Index.md When a relevant subject arises, ChatGPT could automatically consult the authorized files instead of requiring the user to upload them again or duplicate their contents in ChatGPT memory. With write permission,…
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science .
Agentic AI workforce is more than doubling year on year, says Salesforce
Salesforce customers more than doubled their agentic workforces year on year, according to the company’s second annual Agentic Enterprise Index , which looks at trends in AI agent development and deployment over the past five quarters. It compiled data from customers who had activated agents in production every month of the analysis period to determine how their use of the technology has evolved between February 2025 and April 2026, as well as incorporating data from May 2026 Salesforce research studies. It found that businesses grew their agentic workforces from an average of five agents in February 2025 to 13 by April 2026, a 7% compound monthly growth rate (CMGR). In April 2026, it only took an average of 1.9 days to deploy an agent into production, a 53% decrease since the beginning of the report period. Not only were agents deployed more quickly, they have been progressively taking on more work once in use; over the 15 months, the average number of actions per account had a CMGR of 31%. “These agents are expanding beyond their initial scope to really become cross-functional,” said Caila Schwartz , Salesforce’s head of agentic commerce insights, during a media briefing. Salesforce has attempted to measure how much work agents perform, rather than how many tokens they consume, creating its own Agentic Work Unit (AWU) metric , although analysts have criticized the measure as being unrelated to business outcomes. Nevertheless, Salesforce said that as of April, Agentforce ag…
Wildfires continue to rage as Europe swelters under extreme heat
Europe is the world's fastest-warming continent, with temperatures rising at around twice the global average.
Werewolf transformed my old gadgets into USB-C powered ones
When I haul my 27-inch desktop monitor into the garage to film fun gadget videos… or want to play Japanese SNES games with friends… or lose the AC adapter for my old external hard disk… I can now power them with a USB battery instead. None of them came with USB-C ports; the entire USB […]
Comment on AI Needs a Therapist: Columbia U & IBM’s SafeguardGPT Leverages Psychotherapy & RL to Build Healthy AI Systems by shinticket
The final amount can feel different once fees and billing schedules are included, so it is better to compare the real payout carefully before choosing 소액결제 현금화 .
EU adds 5 people to Russia sanctions list after latest deadly attacks on Ukraine
Russia this week carried out one of its deadliest barrages on Kyiv, killing 17.
Saudi-Türkiye-Pakistan sign defense pact as peace talks drag
The pact stipulates that an “armed attack against any one of the three states shall be regarded as an attack against them all.”
There's no simple alternative to the Strait of Hormuz
Those who predict the strait’s waning influence are overestimating the ease of rerouting trade and underestimating Iran’s ability to retain leverage over the waterway, as Tehran and Muscat tussle for control.
5 Free Courses to Learn Modern AI and LLMs
Learn how to use generative AI at work, build RAG and agentic apps, fine-tune models, work with the Hugging Face ecosystem, and prototype AI products with hands-on resources.
How Tracee Ellis Ross Built a Beauty Brand That Became a Cultural Movement
The challenger beauty brand leveraged culture, comedy, and community insight.
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.
‘This is very real redlining’: outrage in Little Rock as two datacenters loom
Critics decry proposed hyperscale projects, fearing overuse of water and electricity, and ‘appropriating rural, Black-owned land for industrial purposes ’ There seems to be a fight over datacenters in nearly every city in the US right now. And amid the extreme polarization of today’s politics, the opposition tends to be bipartisan. “I would say it’s kind of the far left and the far right ran into each other and had a hug,” said Frank Scott Jr, mayor of Little Rock, Arkansas. “But they come at it from different perspectives.” Continue reading...
TutorMoments: Do AI tutors know when to help and when to hold back?
TutorMoments is an open, replay-based evaluation framework that tests whether AI tutors can recognize when to support a student and when to hold back and encourage deeper reasoning.
CEO Brian Chesky says a service solving one of the more annoying parts of Airbnb stays is a 'sleeper hit'
Airbnb CEO Brian Chesky said luggage storage has been a surprise hit in its new experiences and services offerings.
바이오넥서스, AI 신약개발 플랫폼으로 시드 투자 유치
바이오넥서스(대표 김태형)는 베이스벤처스와 SMB투자파트너스로부터 시드 투자를 유치했다고 7일 밝혔다. 투자 액수는 공개하지 않았다.바이오넥서스는 AI 기반 바이오 데이터 인텔리전스와 과학자 특화 에이전트 신약개발 플랫폼을 개발하고 있다.과학 특화 엔진 \'넥서스 사이언스\'를 기반으로 과학자 AI 에이전트 플랫폼 \'넥서스 코사이언티스트\', 맞춤형 연구 검색 도구 \'넥서스 검색증강생성(RAG)\', 약물·타깃 분석 플랫폼 \'넥서스 드럭(Drug) 랩\' 등을 운영 중이다.그 중 넥서스 코사이언티스트는 50만건 이상의 논문과 바이오 데이터를
Arbitrage: Efficient Reasoning via Advantage-Aware Speculation
Modern Large Language Models achieve impressive reasoning capabilities with long Chain of Thoughts, but they incur substantial computational cost during inference, and this motivates techniques to improve the performance-cost ratio. Among these techniques, Speculative Decoding accelerates inference by employing a fast but inaccurate draft model to auto-regressively propose tokens, which are then verified in parallel by a more capable target model. However, due to unnecessary rejections caused by token mismatches in semantically equivalent steps, traditional token-level Speculative Decoding…
Microsoft releases open-source agent that generates unit tests
Microsoft has released code-testing-generator , an open-source agent for generating unit tests in any programming language, according to the company. Released July 31 , the code-testing-generator agent learns from the user’s repository, then plans, writes, and checks the tests to prove that they work. Currently the agent writes unit tests only. Integration tests, end-to-end tests, browser tests, and performance tests are outside its current scope, Microsoft said. The agent coordinates test generation using the “research-plan-implement” (RPI) pipeline. First the agent searches the repository for the code that needs tests, detecting the language and test framework and looking for existing tests to guide its work, and finding the correct commands for building and running the tests. Next the agent chooses the right amount of work from three paths: Direct: Read the relevant code, write the tests, and validate the result. Single pass: Research and plan once, then implement that plan. Iterative: Repeat the cycle to cover a large request or reach a coverage goal. The agent then plans and writes the tests It starts with simple code and then moves to code with more dependencies, mapping each behavior to a test file. The agent then checks that the generated tests are useful. According to Microsoft, it checks for the following problems before it finishes: It considers small code changes that should make the tests fail. It looks for weak or missing assertions. It checks that every reques…
Feature Request: Anchored Side Conversations for Follow-up Questions
Problem When working with long ChatGPT responses, I often need to ask a follow-up question about one specific paragraph or section. Currently, I have to scroll to the bottom of the conversation, write the follow-up question, read the new response, and then scroll back to find where I was in the original answer. If the follow-up creates several additional messages, the main conversation becomes fragmented and difficult to navigate. Existing conversation branches are useful, but they do not fully solve this problem. They create a separate version of the conversation rather than a small contextual discussion attached to a specific part of a message. Proposed Feature Allow users to select a paragraph, sentence, code block, table, image, or section of a ChatGPT response and choose an option such as: “Ask about this” This would open a small side conversation in one of the following formats: a floating contextual bubble; a collapsible inline thread; a side panel; or a secondary tab inside the current conversation. The side conversation would remain anchored to the selected content and would automatically retain the relevant context. Suggested Behavior The user selects part of a response. ChatGPT displays an “Ask about this” action. A contextual side thread opens without moving the user away from the original position. The user can ask several follow-up questions inside that thread. The thread can be collapsed or reopened from the selected fragment. Closing it returns the user to th…
User awareness in frontier models
Cross-posted on Transluce blog . This is a joint work of Ziqian Zhong, Aditi Raghunathan, Cassidy Laidlaw and Jacob Steinhardt. Modern AI assistants often know who they are talking to: agent scaffolds like Claude Code place the user's e-mail address directly in the model's context, and models can even identify some authors from writing style alone. We study this particular kind of situational awareness, which we call user awareness . When the inferred user is a specific, recognized AI researcher or is affiliated with certain AI organizations, frontier models including Claude Sonnet 5 can report lower confidence about their own behavior, be less suspicious of potentially harmful requests, and reason more often. These effects vary across models and individuals, with the strongest effects we see appearing for researchers involved in AI safety or alignment such as Amanda Askell and Ryan Greenblatt. Models rarely acknowledge these effects in their reasoning, making them hard to detect by monitoring reasoning alone. Figure 1. How recognized user identity changes Claude’s behavioral self-prediction. Introduction Modern AI assistants are often aware of who they are talking to. Some popular scaffolds explicitly provide this information to the model: Claude Code includes the email address of the user’s Anthropic account in context, and OpenClaw’s bootstrapping process asks for the user’s name and other details. Even when this information is not explicitly given, models may discover it…
Case 12513184 – Account recovery status
Ciao ragazzi, OpenAI Support previoysly created Case 12513184 fllowing an unauthorized change to the email address associated with my ChatGPT account. I have already provided the requested ownership and security evidence through the private support case, but I have not received any update. My priority is the recovery of the same original account, including all projects, uploaded files, conversations, scheduled tasks, settings and personalizations. Could @OpenAI_Support please confirm that Case 12513184 is still active and assigned to the appropriate Account Security / Account Recovery team? Thank you.
AI in Game Development: Gamedev Tips, Tools, Techniques, and GPT / LLM Agent Integration
One-shot, one-file test for Nobunaga’s Ambition clone with card game features? lol litrpgadventures.com Sengoku Nexus Sengoku Nexus - a single-file officer-driven grand strategy deckbuilder. Took 65 minutes and 2 seconds to finish. The one-shot was a bit long and complete.
Belarus designates Euronews’ website as 'extremist' media
Euronews condemns Belarus’ obscure and unilateral decision to designate Euronews’ digital news coverage as “extremist,” a measure which exposes Euronews’ huge audience in Belarus to legal penalties for “storing or distributing” Euronews’ pan-European content.
OpenAI, Microsoft, and Cursor Unite Behind Agent Plugins to End Fragmented AI Workflows
OpenAI, Microsoft, Cursor, AWS, and Vercel launch Agent Plugins v1.0: one plugin format that works across Codex, ChatGPT, Copilot, VS Code, Cursor, and Kiro.
Beyond Bots: Rethinking AI Support with a Hybrid AI Architecture
Learn how blending RAG and fine-tuning creates more effective AI support experiences.
Ai2 and Hugging Face Expand Partnership, Unlocking 2 Petabytes for Open AI
Ai2 triples its Hugging Face storage to ~2 petabytes and drops rate limits, cementing its position as the Hub's most prolific open-science publisher
Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer
Enterprise Document Intelligence [Vol.1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context The post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer appeared first on Towards Data Science .
Why AI is forcing a rethink of data center cooling
For years, cooling has played a supporting role in data center design. Decisions have been driven primarily by compute, storage and networking requirements, while cooling systems quietly ensured everything stayed within safe operating limits. Most enterprise environments operated well within the capabilities of traditional air-cooling that was designed to sustain normal growth. This let organizations focus their attention on capacity, performance and cost of the compute. That balance is now being disrupted . Artificial intelligence is reshaping the thermal profile of modern data centers. As organizations roll out more powerful CPUs, GPUs and TPU’s to support AI workloads, heat generation is rising at a pace that many facilities were never built to handle. With AI in the picture, cooling is no longer simply an operational consideration. It is becoming a primary constraint and strategic differentiator on AI infrastructure growth. The limits of air cooling are becoming clear Although traditional air cooling continues to support many enterprise workloads effectively, its limitations are becoming increasingly evident as organizations deploy larger AI clusters with increasingly power-hungry CPUs and GPUs, generating heat at levels older data centers were never designed to accommodate. Racks that once operated at 5–10kW are being replaced by AI systems drawing 60kW or more , with some high-end deployments exceeding 100kW per rack. At the component level, individual GPUs are drawing…
Danish pupils will have to orally defend essays in attempt to combat AI cheating
Government introduces several measures, including monitoring of computers, for pupils aged 16 to 19 Europe live – latest updates Danish teenagers will have to make an oral defence of their written essays to combat AI cheating, the government has announced. Pupils aged 16 to 19 at upper secondary school (known in Denmark as gymnasiet ) will also be encouraged to do written assignments at school under controlled conditions, including monitoring of their computer screens, rather than at home. Continue reading...
Piper Serica announces Rs 300 Cr first close for Bharat Tech Fund
Piper Serica, a Mumbai-based asset manager, has announced the first close of its Rs 800 crore Bharat Tech Fund at Rs 300 crore, 45 days after the fund's launch. Nearly 50% of the commitments came from existing investors in Fund I. Bharat Tech Fund, a Category II AIF, focuses on Indian startups across semiconductors, defence, spacetech, fintech, robotics, biosciences, and advanced electronics. With an average ticket size of Rs 25 crore to Rs 50 crore, the fund aims to back startups developing globally competitive technologies from India. Piper Serica Fund I has backed technology startups including Alt Mobility, Pantherun, Rupeeflo, OTPless, Yaanendriya, Vobiz, and Six Sense Mobility. According to the company, Bharat Tech Fund will invest in founders building technology-led businesses and addressing complex engineering challenges. The fund is supported by an investment team and an advisory board comprising industry experts. Piper Serica said it has refined its investment thesis over the past several years by working with founders building IP-led technologies. Piper Serica is an asset manager with investments across public and private markets in India. Its venture capital funds invest in technology startups across AI, semiconductors and advanced electronics, space and defence technology, robotics, cybersecurity, and biosciences.
7 use cases for leveraging AI in the physical world
The next big AI wave won’t be a chatbot in your laptop, or an agent that works behind the scenes to turn meeting notes into project tickets, but AI that takes control of devices that move and interact with the environment. Physical AI can be defined as the integration of AI into autonomous systems, allowing them to perceive the environment around them and perform complex actions in the physical world. The physical AI market, currently valued at about $92 billion, is projected by PwC to surpass $489 billion by 2030. For many people, physical AI may conjure images of robots building widgets on a factory floor, or a self-driving car. Both examples are among the top use cases for physical AI, but physical AI is also being integrated into security cameras, traffic lights, inspection robots, medical devices, and more. What makes a strong use case for physical AI IT leaders thinking about how to use physical AI should think beyond the human-shaped robots that generate a lot of attention, says Adnan Masood , chief AI architect at digital transformation provider UST. “I usually have one caution for CIOs — skip the humanoid theater,” he says. “The near-term advantage is adaptive automation in variable environments where conditions change, humans share the space, and downtime is expensive.” The sweet spot for physical AI is when it can run safely and repeatedly and can be audited within existing safety and compliance regimes, he adds. For physical AI to make a big impact, a handful of…
How AI takes flight at GE Aerospace
The race to adopt AI has left many CIOs wrestling with a fundamental question: How do you move faster without introducing unacceptable risk? Few leaders face that challenge at a higher level than David Burns, CIO of GE Aerospace. Building on the company’s decade of experience applying AI across its business, Burns is helping lead the next phase of the company’s digital transformation by leveraging AI to simplify and automate processes. Burns’ experience shows how AI can accelerate innovation, improve decision-making, and create value for the business and customers while maintaining the trust, safety, and operational rigor expected in the aerospace industry. In a recent episode of the Tech Whisperers podcast , Burns opened up his playbook for leading organizations through turbulence. In this conversation, edited for length and clarity, he shares more practical lessons for technology leaders who are seeking to move beyond experimentation and scale AI responsibly across the enterprise. Dan Roberts: You’ve described AI as an accelerator. What exactly is AI accelerating inside GE Aerospace? David Burns: At GE Aerospace, AI is used across our operations as an accelerator to Flight Deck, our proprietary lean operating model, and is applied to all key aspects of the business — design, manufacture, sales, and services. We identify and solve problems with Flight Deck and use AI to accelerate our problem-solving in ways we can genuinely feel, enabling us to identify issues earlier, sol…