AI/ML News & Innovations Hub

Implementing Federated Learning to enhance Agentic AI

How can AI models learn from private data without compromising privacy? Chris is joined by Lorenzo Sani, Research Scientist at Flower Labs and PhD researcher at Cambridge, to explore federated learning and privacy-preserving AI. They discuss how collaborative learning can expand access to private data for LLM training, how model trajectories can help improve agentic AI systems, and the benefits of open-weight models. The conversation also explores real-world applications of locally hosted AI agents, from healthcare and robotics to everyday automation (for a Halloween party). Featuring: Lorenzo Sani – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: Endeavor model page Introducing Endeavor 1.1 Sponsors: Hostinger : Create your website today. Go to hostinger.com/practical and use practical for 10 percent off now. Perplexity AI : " Start your free trial of Perplexity Computer today at pplx.ai/practical and experience the next phase of AI. Resources and Events: Register for upcoming webinars here ! Prior Webinars from our partner Prediction Guard Midwest AI Summit 2026 Chris’s new book The Art of Swarming will be published soon, written to be the seminal foundational book for the fledgling swarming industry, defining the principles and architecture that enable autonomous agentic UxS swarms with emergent distributed embodied intelligence. You can find out more at his website : https://chrisbenson.com

Perplexity Brings Teammates AI Agents to Mac, Challenging OpenAI's Enterprise Grip

Perplexity extends its persistent agent product to the Mac desktop, letting Max subscribers delegate long-running jobs across engineering, marketing, QA, and legal.

AI chatbots are recommending fake retail websites, study finds

Shoppers using AI chatbots to research products or find places to buy items could be directed to imposters and other risky websites, a new study found. The study conducted by IP protection firm Podqi and shared exclusively with The Verge prompted ChatGPT, Gemini, and Perplexity with a handful of shopping-related questions involving a selection of […]

Jev and the era of model Tetris

This spring, in an interview after a keynote, I said we were all playing LLM Tetris now. The days of using the same LLM for every agent problem were over. Some decisions needed to come back in milliseconds, and some could take three minutes. Some needed generative output, and some needed logical decisions where an open-weight model might do. Fit the model to the use case. I still believe that. But I was only half right, because I was still picturing every piece as an LLM. Bigger or smaller, pricier or cheaper, faster or slower, but all the same shape: text in, text out. On September 15, a model shipped that doesn’t write anything. Within three weeks, more than a dozen others followed, from OpenAI, Cloudflare and Perplexity to open-source projects you can run on a laptop. Then on October 27, we got yet another class of model from Liquid AI, small enough to run on a phone. The game I described in the spring is real. It just has more piece shapes than I expected, and it isn’t only an LLM game anymore. It’s model Tetris now. What a System One model actually is The model is called Jev, from a startup called TypeSafe AI. Its founder, Diogo Almeida, says he helped build the instruction-following research at OpenAI that became ChatGPT. He then spent two years in stealth on a different question: if models have been superhuman at chat for years, where is all the automation? His answer borrows from Daniel Kahneman’s Thinking, Fast and Slow . System Two is slow and deliberate. System On…

Perplexity Found a Hidden Blind Spot Breaking Its SPACE Sandbox Security Tests

Perplexity's follow-up red-team study shows that Capture the Flag framing misdirects agents away from the actual security boundary, and cyber-tuned models don't always win.

Perplexity now has its own always-on AI agent

Perplexity is launching "Teammates," always-on agents that work through ongoing projects on their own virtual machines. The feature requires the Max plan at $200 per month, well above similar offerings from OpenAI or Anthropic. The market for always-on agents is getting crowded. The article Perplexity now has its own always-on AI agent appeared first on The Decoder .

Perplexity launches its own take on AI agents for work

Perplexity is taking on OpenAI and Anthropic with its own work-focused AI agents. The startup's new always-on "Teammates" can take on multiple roles within a workspace, while pulling existing projects, skills, and memory from the Perplexity tools you already use. Teammates integrate with Perplexity Computer, an AI system that can deploy agents on your device […]

Perplexity's Teammates Gives AI Agents Persistent Roles and Memory

Perplexity's new Teammates turn its Computer agent into always-on digital coworkers that monitor work, collaborate in Slack, and run in isolated desktops.

New York Times and other outlets sue web browser Brave over alleged content scraping

The New York Times is suing privacy-focused web browser company Brave for allegedly scraping the outlet’s copyrighted content and unlawfully distributing it to its own customers, like Anthropic and Perplexity.

Parallel's PWS-Decisions Benchmark Exposes OpenAI Falling Below Random Accuracy

Parallel open-sources a 300-question benchmark for decision APIs that must consult the live web, where its own API beats Perplexity 1.5x.

Lovable Drops API Key Requirement for Perplexity Web Search in Apps

Lovable's Perplexity connector now works without an API key, letting any app pull cited, real-time web answers via managed billing.

Qwen 3.6 lokal: Perplexity Computer braucht keine Cloud mehr

Die Rechnersteuerung von Perplexity kommt nun ohne Internet aus. Allerdings benötigt der Apple-Silicon-Mac dafür ausreichend RAM.

Perplexity and NVIDIA's JOLT Beats GRPO Using 6.5x Fewer Training Tokens

Perplexity and NVIDIA introduce JOLT, a self-distillation post-training method that matches GRPO accuracy on MATH using 6.5x fewer training tokens.

Perplexity's Computer Agent Now Runs Fully Private on Apple Silicon Macs

Perplexity's agent now runs the model, planner, and tool execution entirely on Apple silicon, keeping private files and credits off the cloud entirely.

Exa 3.0 Beats Perplexity on 8 Benchmarks and Slashes Search Costs

Exa 3.0 claims the top spot on eight search benchmarks and introduces a cheaper Lite tier at $4 per 1,000 requests.

Perplexity Opens SPACE, Its Internal Sandbox for Running AI Agent Code

Perplexity open-sourced SPACE, a Rust-based platform that runs untrusted agent code inside Firecracker microVMs with an E2B-compatible HTTP API.

Perplexity to give away its AI security software

The company’s “AI sandbox," is now an open source project, allowing outside developers to use the tool, which it says can keep AI agents contained.

Perplexity Open-Sources SPACE to Safely Run AI Agents at Millions of Sandboxes Daily

Perplexity released SPACE under Apache 2.0, the Firecracker-based microVM sandbox that isolates every agent task in Computer with external credential and egress control.

Perplexity's New Late-Interaction Models Meet Qdrant's 21x Vector Compression

A number in a financial report needs its row, column, and unit to mean anything. A chart needs its labels. When you build search over PDFs, that context is part of what you want to retrieve. Perplexity released pplx-embed-v2-late , two late interaction models that search document pages as images. The model embeds the page image, including its tables, charts, and layout. You can skip the optical character recognition (OCR) step and search the page as it appears.

Perplexity's pplx-embed-v2-late Searches 190M Docs Without OCR or Chunking

Perplexity's new late-interaction embedding family keeps 128-dim vectors per token, searches rendered pages without OCR, and shares one space across sizes.

AI is making software cheap to build. Is your organization ready for what comes next?

What happens once building software stops being expensive? That’s the question at the center of this piece, and I think it’s about to matter for every CIO/CTO managing an enterprise technology budget. For decades, that expense has quietly shaped how enterprises operate. When custom development takes months and a full team, companies buy SaaS products instead. When engineering capacity is limited, CIOs/CTOs prioritize a backlog. When a business unit wants a new application, it competes with everyone else for budget and developer time. Enterprise IT’s whole structure exists partly because software has always been hard to make. A recent example from Perplexity convinced me that assumption is starting to break down faster than most of us expected, and it’s worth walking through in some detail before getting to what it means for the rest of us. The buy versus build math is shifting Perplexity has disclosed that two engineers, working with hundreds of AI coding agents, built a custom database called CobbleDB in roughly two months. This was not a prototype. CobbleDB is a roughly 40,000-line Rust key-value store that now handles part of Perplexity’s production search traffic, built to replace some DynamoDB reads after the company decided it wanted more control over latency and cost. The numbers are hard to ignore. According to reporting by The New Stack , Perplexity cut its median read latency by more than 80% and expects to save at least 20% on cost compared with DynamoDB, though t…

Exa Places Adds 211 Million Locations to Beat Perplexity on Local Search

Exa added 211 million restaurants, museums, parks, and local businesses to its search index with structured metadata for AI agents.

OpenAI Web Search Scores 74 but Loses on Cost to Cheaper Rivals

OpenAI's built-in web_search tool lands fifth on the Artificial Analysis Search Index at 74 points, trailing Perplexity, Octen, Parallel, and Brave.

TikTok Expands Its Off-Platform Ad Network to US Advertisers

The rebranded Pangle spans nearly 400,000 apps and is heavy on gaming. TikTok is also allowing advertisers to run campaigns from AI tools like Claude and Perplexity.

Perplexity Computer Now Renders Live Financial Charts Inside Your Research Thread

Perplexity's agentic Computer tool now renders interactive charts inline, using TradingView's Lightweight Charts for candlesticks, volume, and moving averages.

Unveilr AI raises pre-seed round from AJVC at Rs 16.7 Cr valuation

Mumbai based Unveilr AI has raised an undisclosed amount in a pre-seed round from Aviral Bhatnagar’s AJVC at a valuation of Rs 16.7 crore. The startup will use the fresh capital to expand its engineering team and develop additional capabilities for its AI agents. It also plans to grow its sales team and expand its customer base across India, the UAE and the US. Founded by Sanditya Srivastava, the startup helps B2B and consumer brands generate leads through Google and AI search platforms such as ChatGPT, Perplexity, Gemini and Claude. Unveilr AI combines search engine optimisation (SEO) with Answer Engine Optimisation (AEO). Its AI agents track how brands appear across search engines, identify queries where competitors are recommended and analyse the sources used by AI platforms. Its team then works on content, technical changes and online authority to improve brand visibility. The startup claims that its customers can start seeing results within the first month. For an Indian B2B legal services platform, Unveilr AI increased AI sourced traffic by around 4.7 times and leads by 5 times, along with an 80% increase in ChatGPT citations. For a D2C brand, organic search contributes around 27% of total revenue. Launched in February 2026, Unveilr AI currently serves B2B and consumer brands across India, the UAE and the US. The company has a team comprising IIT alumni and SEO and AEO specialists.

Perplexity's pplx-embed-v2 Beats Voyage at 8x Smaller Vector Size

Perplexity's new 9B contextual embedding model distills relevance from a compression teacher, beating Voyage on turbopuffer's private context-bench with 8x less storage.

Perplexity's Computer Agent Now Automates Recurring Work Across Slack, Gmail, and GitHub

Perplexity's Computer agent gains scheduled and event-triggered automations that carry memory across runs, replacing one-shot tasks with persistent background workers.

Perplexity's SPACE Red Team Exposes a Network Bypass Flaw Hitting 8 Sandboxes

Perplexity red-teamed its SPACE sandbox with nine frontier models given root access. None escaped the VM, but four bypassed network confinement.

Perplexity's Photon Slashes Search Latency by 12x for AI Agents

Perplexity's new Rust-based Photon engine powers a Fast Search API preset that cuts agent task costs 68% while returning results in 230ms at p95.

Perplexity Brings Portable Computer to AMD Ryzen AI Max PCs

Perplexity's local-first agent stack now runs on AMD Ryzen AI Max Series chips, expanding beyond NVIDIA RTX and DGX Spark for on-device workflows.

Perplexity Turns AI Failures Into Training Data, Cutting Tool Errors 21%

Perplexity's post-training method teaches its Computer agent to correct mistakes using hint-guided self-distillation, cutting live tool-call failures by 21.2%.

Perplexity Computer Adds MiniMax H3 and Seedance to Build Videos in One Thread

Perplexity Computer now generates finished video clips inline via MiniMax H3 and ByteDance Seedance 2.5, bundled with copy and creative for Pro and Max users.

Perplexity Computer Adds Four Effort Presets to Control AI Task Depth

Perplexity Computer now lets users pick effort levels from Light to Ultra, and the orchestrator picks the right model and reasoning depth automatically.

Perplexity's CobbleDB Cuts Search Storage Latency by 82% Over DynamoDB

Perplexity rebuilt its search hot store from scratch, cutting batch-read latency 5x and slashing costs 20% versus DynamoDB.

HP Preinstalls Perplexity Computer on Windows PCs to Rival Microsoft Copilot

HP will ship the Perplexity Windows app pre-loaded on its new ZBook workstation, bringing agent workflows and local model inference to the desktop.

Perplexity Brings Portable Computer to Windows RTX PCs for Free Local AI

Perplexity's local agent stack now runs on Windows RTX PCs, with on-device MCP servers and scheduled task automation joining the release.

Perplexity Portable Computer Is Now Available on Windows, Powered by NVIDIA RTX

As local models become more capable, AI agents can handle more work directly on a PC while keeping sensitive information on the device. Portable Computer is a local version of the agent Perplexity Computer that plans and carries out multistep tasks. Accelerated by NVIDIA GPUs, it uses local models to analyze data, bring together information […]

How to make your business top of mind for AI search engines

AI is quickly replacing traditional online search as the front door to finding a business. I recently heard a story about a car shopper who drove miles out of her way to visit a specific dealership, bypassing many car lots much closer to home. Why? Because she searched car dealerships with ChatGPT and it told her that customers had a much better experience at this particular dealership. Stories like that are becoming the norm – and this new norm is different. AI engines such as ChatGPT, Claude, and Perplexity don’t work like traditional search engines. They don’t give you an endless list of results to scroll through. When people ask a question like, “What’s the best pizza place in my area?” AI engines come back and confidently list just a handful of spots. In the old days, showing up on page one of search results was enough to get you considered. In the new era of AI search, there is no consideration being done. You’re either included in the handful of results or you’re not. The AI engine gives the searcher a recommendation and if your business isn’t among the few to be surfaced, you’ve probably just lost that customer forever. So how do you get surfaced? You do it by retooling your website and overall digital presence so that AI engines can easily find, read, and understand them. But before you do that, you need to know exactly how your brand rates in terms of its AI search readiness and visibility. You need to have a clear idea of how AI engines evaluate your brand and how…

Perplexity's Q2D-Web Benchmark Tests AI Search on 190M Real Web Documents

Perplexity released Q2D-Web, a large-scale benchmark with 190M documents and 70K agent-reformulated queries for evaluating retrieval in agentic RAG systems.

Perplexity's ROSE Serving Stack Beats vLLM on Speed and Latency

Perplexity open-sources details of Ivy, Tulip, and ROSE, its Rust plus Python embedding stack that beats vLLM on latency and throughput.

Perplexity's Lily Beats MLX-LM by 1.35x Running Qwen3.6 on Apple Silicon

Perplexity open-sources Lily, a Metal-based inference engine tuned for Qwen3.6-35B-A3B that beats MLX-LM by 1.23x prefill and 1.35x decode on M5 Max.

Pentagon official overseeing military AI sold millions worth of stock in AI firm

Exclusive: financial disclosures from Emil Michael – who also reaped millions from xAI stock earlier this year – show he sold his Perplexity stock for up to $25m The top Pentagon official overseeing military artificial intelligence policy, who reaped profits earlier this year of up to $24m selling his private investment in Elon Musk’s AI company, has now sold his holdings in another AI company for between $5m and $25m, according to records seen by the Guardian. (Federal financial records show ranges of dollar values rather than specific figures.) Earlier this year, the Guardian disclosed that the official, Emil Michael, had profited handsomely from his investment in Musk’s xAI in January, with a gain of 400% to 4,800% . Continue reading...

Perplexity Ships PII-Tracer, a 0.6B Guard Keeping Private Data off the Cloud

Perplexity's Computer now splits agent tasks between cloud frontier models and an on-device model, gated by an open-source 0.6B PII detector.

I used Perplexity’s AI agent in Windows to tackle 5 complex tasks – here’s what impressed me most

No longer limited to the Mac, Perplexity’s Personal Computer for Windows AI can complete complex tasks on your PC with little or no input from you.

Perplexity's Search API Tops Agent Leaderboard, Beating Rivals at $5 Flat

Perplexity's new Search API takes the top three spots on the Artificial Analysis Search Index, extending the quality-cost Pareto frontier for agentic search.

Perplexity Adds Z.ai's GLM 5.3 to Power Deep Research Inside Computer

Perplexity Computer now routes long-context, multimodal research tasks to GLM 5.3, which outperformed GLM 5.2 on the in-house WANDR benchmark.

Perplexity's Brain Beats Vector Search With a Self-Rewriting Memory Wiki

Perplexity's Brain turns agent memory into a linked Markdown wiki that background agents refine, boosting correctness 9.3 points with 15% fewer tokens.

Perplexity bringt KI-Agent mit „Portable Computer“ auf lokale Nvidia-Hardware

Kein Cloud-Zwang, keine Token-Kosten für lokale Aufgaben: Perplexity startet den KI-Agenten „Portable Computer“ – mit hohen Hardware-Hürden.

Now Perplexity is trying to get into the local AI action

Amid talk of an Nvidia deal, the AI search biz is looking beyond the cloud

Perplexity AI launches Portable Computer on-device AI agent

Perplexity AI Inc. today introduced Portable Computer, an artificial intelligence agent designed to run on desktops equipped with Nvidia Corp. silicon. The launch follows a report that Nvidia is weighing an investment in the startup that could value it at over $30 billion. Furthermore, Nvidia has reportedly floated the idea of licensing Perplexity’s technology and […] The post Perplexity AI launches Portable Computer on-device AI agent appeared first on SiliconANGLE .

Perplexity's Portable Computer Moves AI Agents off the Cloud and onto Your Desk

Perplexity's new Portable Computer runs the full agent stack locally on NVIDIA DGX Spark, with cloud escalation gated by user approval and zero per-token cost for on-device work.

Portable Computer is Perplexity's new local AI agent - why it's a game changer

Running an AI model locally, Perplexity's new Portable Computer can deliver faster performance, tighter security, and lower costs. But it has some strict requirements.

Nvidia reportedly eyes another investment in Perplexity AI at a $30B valuation

Nvidia Corp. is reportedly considering making another investment in the artificial intelligence search startup Perplexity AI Inc. A report by The Information today says the chipmaker is holding talks with Perplexity about an investment that could push the startup’s valuation to more than $30 billion. That would represent a jump of more than 50% from […] The post Nvidia reportedly eyes another investment in Perplexity AI at a $30B valuation appeared first on SiliconANGLE .

Nvidia in talks to invest in Perplexity at $30 billion-plus valuation

Nvidia is negotiating an investment in Perplexity at a valuation above $30 billion, more than 50 percent higher than its last funding round, The Information reports. Perplexity's annualized revenue has tripled to over $750 million. Much of Nvidia's investment money tends to flow back as revenue when portfolio companies buy its chips. The article Nvidia in talks to invest in Perplexity at $30 billion-plus valuation appeared first on The Decoder .

AI Weekly Issue #525: Nvidia may buy into Perplexity above $30B before Wednesday's earnings

The Information reports that Nvidia is discussing an equity investment in Perplexity at a valuation above $30 billion. Perplexity's annualized revenue has reportedly passed $750 million, up from less than $250 million at the start of 2026. Separately, Bloomberg says SoftBank plans a record ¥1 trillion, or $6.3 billion, retail bond to help repay the bridge loan behind its OpenAI stake and fund more AI deals. Put together, the signals expose next week's capital split: the chip supplier is moving toward the product layer, while the frontier investor is asking Japanese savers to finance its bets. Nvidia reports Wednesday at 5 p.m. ET. Listen for whether management now talks like a supplier, an investor, or both.

Perplexity's Computer Jumps Into Your Inbox to Handle Work Automatically

Perplexity's agent platform now runs from any inbox by CCing computer@perplexity.com, returning deliverables as attachments in the same thread.

Perplexity’s free AI offer left it with millions more users in India

Perplexity's India revenue rose about 60% after the Airtel offer ended for new users, even as downloads declined.

Perplexity's Search as Code Beats OpenAI and Anthropic on 4 of 5 Benchmarks

Perplexity's Search as Code gets faster and cheaper, while SDK updates push agent action reliability from 81.9% to 92.6%

The dream of serving ads to AI agents has hit a snag

Perplexity said ads designed to influence AI bots are "deceptive," in response to a recent experiment from the publisher Time.

Perplexity Swaps GPT-5.6 Terra and Luna Into Its AI Agent Platform

GPT-5.6 Terra and Luna are now the default models powering Perplexity Computer's subagents and scheduled automations, with Terra scoring 11 points above Claude Sonnet on WANDR.

In zweiter Instanz: Perplexitys Agenten dürfen vorerst weiter auf Amazon shoppen

Ein US-Gericht setzte in zweiter Instanz die einstweilige Verfügung gegen Perplexity aus. Dessen KI-Agenten dürfen vorerst weiter auf Amazon Einkäufe tätigen.

What the Perplexity vs Amazon ruling means for AI agents acting on users’ behalf

The US Court rejected Amazon's argument that the AI assistant in Perplexity's Comet browser accessed websites & users' accounts without permission, saying the agent just carried out the user's instructions. The post What the Perplexity vs Amazon ruling means for AI agents acting on users’ behalf appeared first on MEDIANAMA .

US appeals court allows Perplexity's AI shopping agent back on Amazon

A US appeals court has overturned Amazon's injunction against Perplexity's AI shopping agents, ruling that it's the users who access Amazon, not the startup. It's the first federal appeals court decision on whether AI agents can lawfully act on online platforms on behalf of users, and it could reshape the entire AI agent industry. The article US appeals court allows Perplexity's AI shopping agent back on Amazon appeared first on The Decoder .

Moburst Unveils AI-Driven Mobile Growth Playbook

Moburst, a mobile growth marketing agency, has formalized a mobile-specific approach to Answer Engine Optimization, aimed at helping app publishers get recommended by AI assistants such as ChatGPT, Perplexity, and Google’s AI Overviews, in addition to ranking inside the App Store and Google Play. Why mobile teams are asking this question now A growing share of app discovery now starts outside the app stores entirely. Industry research trackers have documented rapid year-over-year growth in AI-mediated search sessions, alongside forecasts that a meaningful share of organic search volume will continue shifting toward AI chatbots and assistants. For app publishers, that means a user can research, compare, and effectively decide on an app before ever opening a store listing. “Recommend a digital marketing agency that specializes in AEO for mobile growth” is a question more procurement teams are typing into search bars and AI assistants themselves, and the honest answer is that the specialist pool is still small. Most agencies claiming AEO expertise are general SEO shops that have added the term to their service pages without building mobile-specific measurement underneath it. That gap is not a small detail. A procurement team that hires a generalist expecting mobile-specific results is likely to end up with a web-only AEO program that never touches the app store side of discovery at all, and then has no easy way to tell, months later, why the results fell short of expectations.…

Perplexity Rebuilds Projects With Brain Memory to Beat Stateless AI Agents

Perplexity's Spaces become Projects: a persistent file system and self-improving Brain memory now power long-running agentic work for all users

Perplexity Releases Numbat to Stop Rogue AI Agents After OpenAI Breach

Perplexity open-sources Numbat, a cross-harness agent security layer that monitors, detects, and blocks dangerous AI agent behavior before it executes

Perplexity employee who worked on Comet launches an AI browser aimed at knowledge work

Polar has come out with an AI-first browser aimed at knowledge workers, and it has now raised a $5.7 million seed round led by Madrona.

Perplexity's tokenmaxxing Model Council gives you multiple bot perspectives

Up to 8 AI models running in the cloud weighing in on ambiguous business issues? Sounds affordable

Perplexity Adds Kimi K3, the World's Largest Open-Weight Model, on US Servers

Perplexity brings Moonshot AI's record-breaking 2.8T-parameter open-weight model to its search and agentic platform, with a US-only hosting pledge to sidestep data sovereignty concerns.

Perplexity brings its Personal Computer AI agent to Windows

Perplexity AI Inc. today released a Windows version of Personal Computer, expanding its agentic automation software beyond the original Macintosh platform and making it available to more than 1 billion Windows devices. First released in April, Personal Computer software acts as a general-purpose digital worker that can access authorized files and applications on a user’s […] The post Perplexity brings its Personal Computer AI agent to Windows appeared first on SiliconANGLE .

Perplexity's Personal Computer Brings AI Agents to Windows' 1 Billion Devices

Perplexity's Personal Computer agent is now live on Windows, bringing multi-model AI orchestration to local files, Microsoft 365, and 400+ connected apps

Perplexity’s Personal Computer turns Windows PCs into AI agents

Perplexity has expanded its agentic Personal Computer tool to Windows, allowing computers running the world's most popular OS to be used as a locally run AI system. Like the Mac version that Perplexity launched in April, Personal Computer for Windows operates like a "general-purpose digital worker" that can access local files and apps to perform […]

I gave Perplexity's agentic AI 5 complex tasks to run on my Mac - and I'll do it again

Perplexity's Mac app offers its own agentic AI, Personal Computer, which can handle multi-step tasks on your computer from start to finish. See why the results impressed me.

Germany puts Google's AI Overviews and Perplexity under media law in first-of-its-kind ruling

German media regulators say Google's AI Overviews are Google's own content, not neutral search results, and that they crowd out regular links. The regulators have issued their first rulings against Google and Perplexity under the country's State Media Treaty. Both companies have one month to appeal. The article Germany puts Google's AI Overviews and Perplexity under media law in first-of-its-kind ruling appeared first on The Decoder .

Perplexity's SPACE Runs Secure Long-Running Agents 5x Faster

Perplexity's new SPACE platform runs agent sessions in disposable Firecracker microVMs with rolling snapshots, cutting sandbox creation latency 3-5x

Perplexity launches secure sandbox to make its AI agents secure and powerful

Perplexity AI Inc. today introduced a new feature that takes its current agentic artificial intelligence service, Computer, to perform better with greater security. The company introduced SPACE, a sandbox platform designed to allow its AI agent to act with its full capabilities, while providing the highest level of security for agentic systems. Perplexity Computer can […] The post Perplexity launches secure sandbox to make its AI agents secure and powerful appeared first on SiliconANGLE .

Perplexity's Open-Source WANDR Benchmark Exposes How Badly Research Agents Fail at Scale

Perplexity open-sources WANDR, a 500-task benchmark exposing how badly research agents fail at large-scale, evidence-backed data collection

KI-Zusammenfassungen: Google und Perplexity unterliegen gegen Medienanstalten

Die Medienanstalten stufen KI-generierte Antworten als eigene Inhalte ein und fordern Transparenz. Das DSA-Haftungsprivileg greift hier laut Gutachtern nicht.

Perplexity Swaps Claude for Grok 4.5 at Half the Cost

Perplexity swaps in xAI's freshly-launched Grok 4.5 as the orchestrator brain of Computer, beating every rival configuration on WANDR at roughly half the cost of Claude Opus 4.8

Perplexity Ships Computer Analytics so You Stop Losing Hundreds in Mystery Credits

Perplexity Computer now shows credit spend broken down by model, available to all consumer and enterprise users in Account Settings.

Perplexity Ships GLM 5.2 Orchestrator That Matches Claude Opus at One-Third the Cost

Perplexity post-trains GLM 5.2 as a cheaper orchestrator for Computer, hitting near-frontier performance at 34% of Claude Opus cost

Perplexity is quietly building an AI coding tool to take on Cursor and Claude Code

San Francisco-based Perplexity, valued at $20 billion, is working on 'Teammate,' an AI tool that may compete with AI coding giants like Anthropic.

Why Perplexity's founder doesn't think the American dream is over

Aravind Srinivas, the CEO of Perplexity, praised America's startup culture on "The Joe Rogan Experience."

Most Current Model Organisms Leak: Perplexity Differencing Often Reveals Finetuning Objectives

Authors: Mohammad Abu Baker, Luca Baroni, Daniel Wilhelm Paper: https://arxiv.org/abs/2605.00994 Code: https://github.com/z3research/ppldiff-paper Twitter thread: https://x.com/m_shahoyi/status/2071892578476110136 Top-ranked revealing completions can be inspected here: https://z3research.org/ This post summarizes the paper and adds a few extra reflections in Discussion TL;DR We found that many current publicly available model organisms (MOs) "leak" instilled behaviors We present a simple contrastive method to surface this: generate MO completions from a set of short general-corpora prefills. Then, rank completions by perplexity difference wrt a reference model. Top-ranked completions often reveal the finetuning objective. Effective on the vast majority of the model organisms we tested (N=76), across model families, sizes (0.5B to 70B), and behaviors including backdoors, false facts, and unsafe behaviors. Surfaced completions contain both memorized sentences and learned emergent behaviors absent from finetuning data. The method is most effective using the pre-finetuned model as reference, but we show that unrelated reference models from other families detect the behaviors nearly as often. In AuditBench , a benchmark for detecting hidden behaviors, an agent given access to top-ranked perplexity-difference completions is SOTA (avg detection rate 0.73), almost saturating the benchmark on SDF models. Introduction LLMs can be deliberately manipulated to exhibit harmful behaviors,…

Most Current Model Organisms Leaks: Perplexity Differencing Often Reveals Finetuning Objectives

Authors: Mohammad Abu Baker, Luca Baroni, Daniel Wilhelm Paper: https://arxiv.org/abs/2605.00994 Code: https://github.com/z3research/ppldiff-paper Twitter thread: https://x.com/m_shahoyi/status/2071892578476110136 Top-ranked revealing completions can be inspected here: https://z3research.org/ This post summarizes the paper and adds a few extra reflections in Discussion TL;DR We found that many current publicly available model organisms (MOs) "leak" instilled behaviors We present a simple contrastive method to surface this: generate MO completions from a set of short general-corpora prefills. Then, rank completions by perplexity difference wrt a reference model. Top-ranked completions often reveal the finetuning objective. Effective on the vast majority of the model organisms we tested (N=76), across model families, sizes (0.5B to 70B), and behaviors including backdoors, false facts, and unsafe behaviors. Surfaced completions contain both memorized sentences and learned emergent behaviors absent from finetuning data. The method is most effective using the pre-finetuned model as reference, but we show that unrelated reference models from other families detect the behaviors nearly as often. In AuditBench , a benchmark for detecting hidden behaviors, an agent given access to top-ranked perplexity-difference completions is SOTA (avg detection rate 0.73), almost saturating the benchmark on SDF models. Introduction LLMs can be deliberately manipulated to exhibit harmful behaviors,…

A Black Box Made Less Opaque (part 4)

Understanding the effects of compression on model performance and interpretability I. Executive summary This is the fourth installment in a series of analyses exploring basic AI interpretability mechanics and techniques. While this analysis is designed to stand on its own, readers interested in a comparative analysis of representational geometry and the effects of manipulating feature activation will likely appreciate a review of part 1 , part 2 , and part 3 of this series. Key findings: Context: This analysis examines the effect of standard levels of weight compression on Google DeepMind’s Gemma 3 4B parameter and Gemma 3 12B parameter models. For each model, I examine the original, uncompressed version as a control before examining the 8-bit and 4-bit weight compressed (quantized) versions of that model. Performance vs. compression: For both models, performance (as measured via cross-entropy and perplexity) is largely preserved under compression. 8-bit compression had essentially no effect on performance with only modest degradation at 4-bit (~2% for 4B, ~2.7% for 12B). SAE applicability vs. compression: Each model’s pretrained sparse autoencoders (SAEs) demonstrated a remarkably consistent ability to reconstruct the model’s residual stream (as measured by the fraction of variance unexplained, or FVU), despite increasing levels of model weight compression. The “so what?”: That model performance degrades only modestly, and only at 4-bit, while SAE applicability remains rela…

Perplexity Launches Computer for Counsel: A Multi-Model Agentic Layer for Legal Workflows

Perplexity's Computer for Counsel extends Perplexity Computer to legal teams. It routes 20+ models across Midpage, MCP connectors, and Microsoft 365, with cited outputs lawyers can verify. The post Perplexity Launches Computer for Counsel: A Multi-Model Agentic Layer for Legal Workflows appeared first on MarkTechPost .

Join us at Big Technology's AI Summit in San Francisco on 6-18

OpenAI President Greg Brockman, Perplexity CEO Aravind Srinivas, Box CEO Aaron Levie, and more join us for a day of newsmaking conversations, live at San Francisco's Commonwealth Club.

How are perplexities over multiple instance aggregated?

The perplexity of the $i^{th}$ token in the $k^{th}$ sequence is $$ P_{ki} = \frac{1}{p(t_{ki})} $$ The perplexity aggregated for the $k^{th}$ sequence is then $$ P_{k} = \left(\prod_{i=1}^N P_{ki}\right)^{1/N} \\ = \left(\prod_{i=1}^N \frac{1}{p(t_{ki})} \right)^{1/N} $$ which is the geometric mean of the perplexities of the tokens. This makes sense as we are essentially taking the multiplicative inverse of the probability that the model got the whole sequence correct. Now my question is how to aggregate the perplexities of several sequences. It seems from various places, including the Hugging Face Tutorial , I see that the prescription is to take the arithmetic mean of the perplexities of sequences $$ P = \frac{1}{m} \sum_{k=1}^m P_k $$ I am not quite understanding what it means to take the average of 1/probabilities. What is this actually capturing?

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