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  1. Rohan PaulProduct release
    AI score53

    Claude for Legal Adds a Library of Prebuilt Mini-Apps Covering Citation Checks and Contract Clause Review

    Claude for Legal has added a batch of prebuilt mini-apps for everyday legal work such as verifying citations and sources and flagging contract clauses that violate company rules, some of which were built by Harvey, PwC, Goodwin, and Foley & Lardner. After opening an app, lawyers can first see how it performs on sample data, then point it at their own files and let Claude complete the first round of processing, with every finding linked to its source. Claude's official account said the app library also covers scenarios such as contract review and regulatory horizon scanning; users can start from the built-in examples, add their own documents and connectors, and share them across the enterprise.

    Quoted postClaude · @claudeai

    New in Claude for Legal: a gallery of ready-made artifacts for contract review, regulatory horizon scans, cite checks, and more. Start from the built-in example, add your own documents and connectors, and share it with anyone across the business. https://t.co/Wn2VK2HeMU

    Same story · 3 reports

    #Product updates#Agents#English text is being prepared.

  2. Rohan Paul
    AI score60

    Cognition Receives Investment Offers at $70 Billion to $80 Billion Valuation

    Cognition has received investor offers valuing it at $70 billion to $80 billion and is seriously considering closing a funding round before the end of the year, but negotiations are still at an early stage and terms may change. The offers came just weeks after Cognition raised more than $2 billion in September at a $48 billion valuation. At $70 billion to $80 billion, investors would be paying roughly 78 to 89 times its revenue, up from 53 times at the $48 billion valuation. The range would also exceed the $60 billion SpaceX paid in stock to acquire Cursor parent Anysphere.

    #Industry news#Agents

  3. Rohan Paul
    AI score81

    Anthropic IPO filing shows $131.7 billion in cash and short-term investments

    Anthropic's IPO filing shows it holds $131.7 billion in cash and short-term investments, a scale exceeding that of Amazon and Microsoft; among U.S. non-bank companies, only Berkshire Hathaway and Alphabet hold more, and it exceeds the combined total of Nvidia and Apple. Its cash and equivalents rose from $17.4 billion at the end of 2025 to $112.4 billion as of June 30, with interest income of $11 billion in the first half of the year, higher than its operating loss of $693 million. Over the same period, undiscounted obligations for leases not yet commenced rose from $50.2 billion to $321.4 billion, with an additional $265.6 billion in fixed or minimum guaranteed commitments; after June 30, it signed new agreements with approximately $245 billion in minimum payments, and Anthropic said it may settle some leases by issuing convertible bonds rather than cash.

    Same story · 127 reports

    #Industry news#Anthropic

  4. Rohan PaulOpinion
    AI score67

    Rohan Paul: The agent economy is taking over, with GitHub commits reaching 7.38 billion in September

    Citing data, Rohan Paul said that GitHub recorded 7.38 billion commits in September 2026, more than five times the figure a year earlier. To handle the flood of agent-generated code, GitHub has added 4 million CPU cores, 160PB of high-speed storage, and 50Tb/s of network capacity this year. A traffic surge on August 17 caused a 7-hour-47-minute outage affecting authentication, Actions, pull requests, and Copilot, and GitHub is migrating to multi-region Azure deployments and refactoring Git to decouple storage from compute.

    Quoted postSatya Nadella · @satyanadella

    We're redefining the SDLC for the agentic era. Lots of GitHub updates this morning at Universe: • GitHub Copilot Fusion delivers frontier-level quality, 40% faster and at up to 77% lower cost • Our unified Copilot harness is now in Foundry, a managed service for building long-running agents that reason, use tools, and get work done • Copilot Projects gives you an orchestration loop to build with multiple agents across repos And we’re expanding capacity and strengthening GitHub’s architecture to make the home of agentic development more resilient.

    #English text is being prepared.#Agents#GitHub

  5. Logan KilpatrickModel releases
    AI score78

    Google Releases Gemini 4 Carbon, Claims SOTA on Cybersecurity and Long-Horizon Tasks

    Google has released Gemini 4 Carbon, claiming it is a major improvement over Argon, which was released two weeks ago. The model achieves SOTA on cybersecurity and long-horizon tasks, ranks No. 1 on Artificial Analysis V5, shows significantly improved usability in areas such as coding, and is more closely aligned.

    Same story · 34 reports

    #Model releases#Google#Evaluations / benchmarks

  6. ARC PrizeFirst-partyAnalysis
    AI score62

    ARC Prize publishes Meta Muse Spark Open's evaluation results on ARC-AGI

    ARC Prize published the validation results of Meta's Muse Spark Open on ARC-AGI: ARC-AGI-1 at 89.0% with $0.29 per task, ARC-AGI-2 at 57.5% with $0.73 per task, and ARC-AGI-3 at 0.1% under the standard harness with $3.9K. ARC Prize states that its v1/2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).

    Same story · 4 reports

    #Evaluations / benchmarks#Reasoning#Meta

  7. ARC PrizeFirst-partyAnalysis
    AI score60

    ARC Prize tests Meta Spark Open: ARC-AGI-2 score 57.5%, cost about 9 times that of models with the same score

    ARC Prize tested Meta Spark Open on ARC-AGI: it scored 89.0% on ARC-AGI-1, 57.5% on ARC-AGI-2, and only 0.4% on ARC-AGI-3. Its v1/v2 scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs. Across providers on OpenRouter, the model costs $1.25/M for input and $4.25/M for output, while Haiku 5.5 and GPT-6 Luna both cost $0.10/M for input and $0.50/M for output. As the reasoning tier increases, ARC-AGI-2 rises from 29.4% at medium (averaging 55k tokens/task) to 57.2% at xhigh (180k tokens/task).

    Same story · 4 reports

    #Evaluations / benchmarks#Reasoning#OpenAI

  8. ARC PrizeFirst-partyAnalysis
    AI score53

    ARC Prize publishes Muse Spark Open reasoning scaling test results on ARC-AGI

    ARC Prize published the test results of Meta's Muse Spark Open on the public ARC-AGI tasks: ARC-AGI-2 improves with reasoning scale, with medium averaging 55k tokens per task for 29.4%, high at 150k tokens for 51.0%, and xhigh at 180k tokens for 57.2%. ARC-AGI-1 already reached 92.8% at high and rose to 94.0% at xhigh. The model scores 57.5% on ARC-AGI-2 at $0.73 per task, and 89.0% on ARC-AGI-1 at $0.29 per task; its scores are close to those of Haiku 5.5 and GPT-6 Luna, but its cost is about 9 times theirs.

    Same story · 4 reports

    #Evaluations / benchmarks#Reasoning#Meta

  9. ARC PrizeFirst-partyAnalysis
    AI score52

    ARC Prize announces Meta Muse Spark Open's evaluation results on various versions of ARC-AGI

    ARC Prize announced the validation results of Meta Muse Spark Open on ARC-AGI: ARC-AGI-1 is 89.0% at $0.29 per task, ARC-AGI-2 is 57.5% at $0.73 per task, and ARC-AGI-3 is 0.4% at $3.0K under the provider adapter harness and 0.1% at $3.9K under the standard harness. ARC Prize states that its scores on v1/v2 are similar to those of Haiku 5.5 and GPT-6 Luna, but the cost is about 9 times that of the latter; the model is priced on OpenRouter at $1.25 per million input tokens and $4.25 per million output tokens, while Haiku 5.5 and GPT-6 Luna are both $0.10 per million input tokens and $0.50 per million output tokens. On public tasks, as the reasoning scale increases, the ARC-AGI-2 score rises from an average of 55k tokens/task corresponding to 29.4% at the medium tier, to 150k corresponding to 51.0% at the high tier, and then to 180k corresponding to 57.2% at the xhigh tier; ARC-AGI-1 has already reached 92.8% at the high tier and rises to 94.0% at the xhigh tier.

    Same story · 4 reports

    #Evaluations / benchmarks#Reasoning#Open-source ecosystem

  11. Garry TanModel releases
    AI score55

    Particle releases its first developer model particle-1, cutting token usage by about 45%

    Suhail announced his new company Particle and its first developer-focused model particle-1, saying the model uses about 45% fewer tokens while maintaining performance, and it is now live. Garry Tan reposted and said Particle will move toward the lowest cost per unit of intelligence.

    Quoted postSuhail · @Suhail

    1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: https://t.co/m5MvOwR9yE https://t.co/qAM7lFUPvG

    Same story · 2 reports

    #English text is being prepared.#Industry news#Model releases

  12. Aaron Levie
    AI score34

    Aaron Levie: Agent token consumption will reach 1,000 times the current level

    Box CEO Aaron Levie predicts that agents will consume 1,000 times as many tokens in the future as they do now, because they will need to run in workflows, process enterprise data, build almost all software, and secure enterprises. He believes that scaling requires simultaneously advancing the capabilities, safety, and efficiency under large workloads of both closed-source model frontiers and open-weight models, and mentioned that Suhail is building Particle for this purpose. Suhail announced the new company Particle the same day, whose particle-1 model uses about 45% fewer tokens while maintaining comparable performance.

    Quoted postSuhail · @Suhail

    1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: https://t.co/m5MvOwR9yE https://t.co/qAM7lFUPvG

    #English text is being prepared.#Agents#Open-source ecosystem

  13. Guillermo Rauch
    AI score12

    Rauchg praises fx's integration with T3 Code

    Vercel Developers announced that the open-source agent framework fx has become a built-in provider in T3 Code, capable of running on web, desktop, and mobile, and compatible with existing models and skills. Guillermo Rauch reposted and commented "Theo cooked".

    Quoted postVercel Developers · @vercel_dev

    fx × T3 Code: our open-source agent harness, fx, is now a built-in provider in T3 Code. • Runs on web, desktop, and mobile • Works with your existing models and skills https://t.co/1wpkvQ0RRg

    #English text is being prepared.

  14. Guillermo RauchProduct release
    AI score60

    Vercel CEO Explains Vercel CI in Detail: Infrastructure, Turborepo Integration, and Agentic CI

    Vercel CEO Guillermo Rauch published a post explaining how Vercel CI solves the AI coding bottleneck, in three layers: fast and stable infrastructure (20 clusters worldwide, running millions of microVMs per day), Turborepo integration (automatically computing the dependency graph and skipping and caching existing work), and Agentic CI. He said that vercel ci in the CLI is the first step; agents can now inspect, abort, trigger, and replay CI, and Vercel Agent can assist with optimization. More capabilities will be added later, giving CI engineer-like judgment. The official Vercel account previously announced that Vercel CI had entered beta, with CI in internal repositories sped up 2-6×, and optimized for agents via Vercel MCP and the CLI.

    Quoted postVercel · @vercel

    Introducing Vercel CI • Intelligently skips tasks with Turborepo • 2-6× faster CI in our internal repositories • Optimized for agents via Vercel MCP and CLI After nearly 8 million tasks, we're excited for the future of agentic CI. In beta on all plans. https://t.co/IcsuvweAhB

    #English text is being prepared.#Agents#English text is being prepared.

  16. Peter Yang
    AI score26

    Peter Yang uses Genie 4 to build 5 worlds in real time

    Peter Yang believes that world models are the next big direction for AI after LLMs. In a new video, he demonstrates how to use Google Genie 4 + Gemini to build 5 worlds in real time, including a monster attacking San Francisco, eating ramen at a street stall in Tokyo, and riding a dragon to attack a castle; he says the visuals are excellent, but what impressed him more was the real-time audio and interactivity, and he shares his views on the impact on film and games.

    #English text is being prepared.#Video#Google

  17. Elon Musk
    AI score34

    Musk reposted the Houston police union's Robotaxi post

    The Houston Police Officers' Union posted that, as Tesla expands its Cybercab/Robotaxi service area in Houston, there are more safe options for getting home after drinking, and called on people not to drink and drive. Musk reposted the post, with only the caption "🇺🇸 Robotaxi Houston 🇺🇸".

    Quoted postHouston Police Officers' Union · @HPOUTX

    There has never been an excuse to drink and drive. With @Tesla expanding their Cybercab/Robotaxi services area across #Houston, there are more ways than ever to get home safely after a night out. Plan ahead, make the smart choice, and never get behind the wheel impaired. #DontDrinkAndDrive

    #Industry news#English text is being prepared.

  18. Elon MuskModel releases
    AI score61

    Google Releases Gemini 4 Carbon Frontier Model

    Google released its new-generation frontier model Gemini 4 Carbon, just two weeks after the release of Argon. The model ranks first with 59 points on the updated Artificial Analysis Intelligence Index v5.0, and in cybersecurity evaluations it scored 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench. Google stated that it shows significant improvement over Argon on long-horizon tasks and has strengthened frontier safety protections; starting this week it will first be made available to select trusted cybersecurity defenders, with the partner scope to be expanded later through the Fairwind Program.

    Quoted postSundar Pichai · @sundarpichai

    I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model. Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching. We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards. As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!

    #English text is being prepared.#Model releases#Google

  19. Rohan PaulProduct release
    AI score50

    Sunra launches a unified API to call image, video, audio and 3D models at the vendors' original prices

    Sunra has released Sunra API, which lets users call all major image, video, audio and 3D models with a single key, billed at each model vendor's own API price, on a pay-as-you-go basis, with no subscription fee and no markup. Rohan Paul relays that Seedance 2.5 costs half as much on Sunra as on two other gateways, and that this unified entry point also makes it easy to run A/B tests on models from Google, OpenAI, ByteDance and Kling.

    Quoted postSunra · @sunra_ai

    Today we're launching the Sunra API. Every leading image, video, audio and 3D model through one key, each at its maker's own API price. Pay as you go, with no subscription and no markup. The models are ready. Your app is next. https://t.co/bQzsZ8DJZy

    #Industry news#Video#English text is being prepared.

  20. Rohan Paul
    AI score38

    Google DeepMind's new paper: CALLIOPE builds topic trees using keywords, outperforming embedding vectors

    A new paper from Google DeepMind proposes CALLIOPE: it has an LLM tag each document with a small number of keywords plus noise, keeps only high-frequency keywords, and then has the LLM propose topics and classify documents accordingly, thereby generating clearer topic maps under strict privacy constraints. Under the strictest privacy setting, human raters preferred CALLIOPE over the embedding-vector-based URANIA on all 3 datasets, with the highest margin being 76% versus 18%. This method consumes 2 to 5 times as many tokens as the latter.

    #Papers/Research#DeepMind#Google