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DeepSeek open-sources the inference engine DeepInfer on GitHub
On October 29, 2026, DeepSeek open-sourced the inference engine DeepInfer on GitHub; this engine is the inference engine behind DeepSeek-V4.1-Flash. Leiphone reported the same day that DeepInfer includes technologies such as MoE expert parallelism, asynchronous batch scheduling, custom kernels, and speculative decoding, and disclosed that a single cluster needs to process approximately 155 trillion tokens per day. Its Zero-Overhead Batch Scheduler lets the CPU schedule computation one round in advance, overlapping with GPU forward passes, and handles cross-round state misalignment through total_inflight_tokens accounting, Remap mapping, and DeferredRelease deferred reclamation. Currently, only offline inference capabilities are open; components such as online KV Cache storage and cross-replica load balancing have not yet been fully disclosed, and the official team has not separately published end-to-end speedup data for this scheduling mechanism relative to synchronous solutions.
On October 29, 2026, DeepSeek open-sourced the inference engine DeepInfer on GitHub; this engine is the inference engine behind DeepSeek-V4.1-Flash. Leiphone reported the same day that DeepInfer includes technologies such as MoE expert parallelism, asynchronous batch scheduling, custom kernels, and speculative decoding, and disclosed that a single cluster needs to process approximately 155 trillion tokens per day. Its Zero-Overhead Batch Scheduler lets the CPU schedule computation one round in a
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Rohan Paul proposes that the agent economy is taking over
Rohan Paul proposes that the agent economy is taking over. He cites data saying that GitHub recorded 7.38 billion commits in September 2026, more than five times the figure a year earlier. To handle the flood of code generated by agents, 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.
Rohan Paul proposes that the agent economy is taking over. He cites data saying that GitHub recorded 7.38 billion commits in September 2026, more than five times the figure a year earlier. To handle the flood of code generated by agents, 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 mul
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The Institute for Advanced Study in Mathematics of Zhejiang University and Zhijiang Laboratory launch the Future Mathematics Program
On October 30, 2026, ScienceNet published an interview report mentioning that the Institute for Advanced Study in Mathematics of Zhejiang University and Zhijiang Laboratory jointly launched the Future Mathematics Program. The report stated that the program focuses on four areas: mathematical foundation models, formal verification, AI mathematical mechanisms, and interdisciplinary empowerment. Li Jianshu, an academician of the Chinese Academy of Sciences, said in the report that AI may change the paradigm of mathematical research, and pointed out that mathematics has done far from enough for AI; Wang Jian, an academician of the Chinese Academy of Engineering, believes that AI's reasoning ability currently comes mainly from code, and that learning deeper reasoning from mathematical data will bring greater gains. The report also mentioned that Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures in May this year, and that OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub, while 25 Fields Medalists including Terence Tao and Deng Yu jointly issued an open letter warning of a serious misalignment between AI and mathematics. Currently, no further progress on the program has been disclosed.
gains. The report also mentioned that Google DeepMind's AI agent solved 9 previously unsolved Erdős problems and tackled 44 open mathematical conjectures in May this year, and that OpenAI recently published 722 mathematical manuscripts generated by its internal AI models on GitHub, while 25 Fields Medalists including Terence Tao and Deng Yu jointly issued an open letter warning of a serious misalignment between AI and mathematics. Currently, no further progress on the program has been disclosed.
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Latent Space announces AIE CODE coding agent conference curation themes
Latent Space host swyx introduces the curation approach for the annual flagship conference AIE CODE, which is single-track, invitation-only, and aimed at about 1,000 agent engineers. This year's three themes are Scaling without Slop, System One models, and Increasing Ambition, among which Jarred Sumner will share Bun's rewrite in Rust and a Bun AOT preview. The article also rounds up that day's AI news, including Mistral's open-weight release and OpenAI's frontier model monitoring.
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DeepSeek integrates Harness with the web, App, and open platform teams
On October 16, 2026, DeepSeek established a product department, integrating its four teams—Harness, Web, App, and Platform—under the leadership of Cui Tianyi, who comes from a quantitative trading background, to oversee product, design, and R&D. Related reports noted that DeepSeek Harness was open-sourced under the MIT license on August 13, and by mid-October the repository had accumulated about 250,000 GitHub stars; the official website has posted job openings for two directions: AI product manager and Agent Harness. The reports also mentioned that Guo Daya, a core contributor to R1, left in March 2026 to join ByteDance Seed because he believed the Agent direction was not a high priority within DeepSeek. Currently, this integration is coordinated by Cui Tianyi, and there are no further developments with supporting evidence at this time.
, design, and R&D. Related reports noted that DeepSeek Harness was open-sourced under the MIT license on August 13, and by mid-October the repository had accumulated about 250,000 GitHub stars; the official website has posted job openings for two directions: AI product manager and Agent Harness. The reports also mentioned that Guo Daya, a core contributor to R1, left in March 2026 to join ByteDance Seed because he believed the Agent direction was not a high priority within DeepSeek. Currently, t
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Nadella's shareholder letter proposes matching token costs to business outcomes
On October 22, 2026, Microsoft CEO Satya Nadella proposed building a frontier ecosystem in the 2026 annual shareholder letter, and put forward "cost-to-outcome" (the business outcomes obtained per unit of token spending) as a new software economics optimization metric. The letter also disclosed that Microsoft's fiscal 2026 revenue was $331.8 billion, up 18% year over year; Azure grew 41% and surpassed $100 billion for the first time; M365 Copilot paid seats exceeded 30 million, and GitHub Copilot users reached 50 million. The letter also stated that interaction is shifting from synchronous chat to asynchronous agents, with Harness separated from models; Foundry offers more than 11,000 models and has over 100,000 customers, and Microsoft Frontier Co. was established, dispatching 6,000 experts to work on-site. Currently, the content of this letter is reported by a single source, with no subsequent independent developments yet.
oft's fiscal 2026 revenue was $331.8 billion, up 18% year over year; Azure grew 41% and surpassed $100 billion for the first time; M365 Copilot paid seats exceeded 30 million, and GitHub Copilot users reached 50 million. The letter also stated that interaction is shifting from synchronous chat to asynchronous agents, with Harness separated from models; Foundry offers more than 11,000 models and has over 100,000 customers, and Microsoft Frontier Co. was established, dispatching 6,000 experts to w
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Podcast: Kimi K3.1 Pro, the new cybersecurity reality, and the Muse user experience
In episode 20 of the Interconnects podcast, Nathan Lambert and Florian Brand discussed how Kimi K3.1 Pro ranks high on Artificial Analysis but receives mixed reviews, arguing that its training leans more toward exploitation than exploration, with weaker performance on long-horizon benchmarks such as ProgramBench. The two also discussed the cybersecurity controversy surrounding open-weight models, noting that no large-scale exploitation occurred after the disclosure of the PHP2Shell vulnerability, and talked about Anthropic not hiring junior employees as well as Florian's experience using Muse to handle everyday tasks such as shopping.
In episode 20 of the Interconnects podcast, Nathan Lambert and Florian Brand discussed how Kimi K3.1 Pro ranks high on Artificial Analysis but receives mixed reviews, arguing that its training leans more toward exploitation than exploration, with weaker performance on long-horizon benchmarks such as ProgramBench. The two also discussed the cybersecurity controversy surrounding open-weight models, noting that no large-scale exploitation occurred after the disclosure of the PHP2Shell vulnerability
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The author comments that REA is essentially an engineering wrapper that chains Ghidra with large models
On October 21, 2026, Ayi AI Notes published an article offering the judgment: the open-source project REA (Reverse Engineer Anything), which has gone viral on GitHub, has not invented a new reverse-engineering algorithm; it essentially receives instructions in the terminal, automatically invokes Ghidra and the file command to decompile into pseudocode, and then lets a large model explain the logic in natural language—an engineering wrapper that strings old tools into an SOP workflow. The author points out that the decompiled variable names may appear to match up, but they are actually blind guesses by the model based on programming habits; the smoother the report, the easier it is to mistake guesses for facts, and using it for security audits carries hallucination risks; the official protocol limits it to research on software one owns or is authorized to use. The author also believes that this kind of wrapper still has reference value for beginners. At present, this judgment appears only in this source, and there are no subsequent developments or facts.
On October 21, 2026, Ayi AI Notes published an article offering the judgment: the open-source project REA (Reverse Engineer Anything), which has gone viral on GitHub, has not invented a new reverse-engineering algorithm; it essentially receives instructions in the terminal, automatically invokes Ghidra and the file command to decompile into pseudocode, and then lets a large model explain the logic in natural language—an engineering wrapper that strings old tools into an SOP workflow. The author
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OpenAI plans to release hundreds of mathematical results
On October 7, 2026, WIRED reported that OpenAI had told relevant parties it planned to release solutions to hundreds of unsolved mathematical problems all at once on GitHub on Tuesday, but the release time had not yet been determined at that point. Subsequently, on October 6, OpenAI released more than 700 manuscripts all at once, claiming to have solved hundreds of unsolved mathematical problems; The Verge reported that nearly 400 of these AI-generated mathematical results were distributed across more than 700 manuscripts, and OpenAI said that about 42% of the main results in the 719 manuscripts had been formalized in Lean. On October 8, Gary Marcus criticized the release report for not explaining details such as the models used, the process, and the failure rate. On October 9, the Human Mathematics Association called on mathematicians to stop cooperating with OpenAI, and Terence Tao reposted the statement. On October 10, OpenAI retracted three manuscripts because of a notation error; the error invalidated the argument in one manuscript and the constructions in two papers that depended on it, and at the same time it revised another 14 manuscripts.
On October 7, 2026, WIRED reported that OpenAI had told relevant parties it planned to release solutions to hundreds of unsolved mathematical problems all at once on GitHub on Tuesday, but the release time had not yet been determined at that point. Subsequently, on October 6, OpenAI released more than 700 manuscripts all at once, claiming to have solved hundreds of unsolved mathematical problems; The Verge reported that nearly 400 of these AI-generated mathematical results were distributed acros
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Simon Willison: October 2026 is the inflection point for vibe coding game development
Simon Willison believes that October 2026 is the inflection point for vibe coding game development. Claude Opus 5.5 has made notable improvements in 3D modeling, music generation, and game design. After having the model make games more fun, he spent a few weeks building four browser games he genuinely wants to play. Each of these projects took no more than four hours of investment, and all were built with HTML and JavaScript and run in the browser; he suggests starting with prompts like "Build a mobile-friendly three.js game where..." and switching to Claude Code or Codex once a project gets past the prototype stage.
Simon Willison believes that October 2026 is the inflection point for vibe coding game development. Claude Opus 5.5 has made notable improvements in 3D modeling, music generation, and game design. After having the model make games more fun, he spent a few weeks building four browser games he genuinely wants to play. Each of these projects took no more than four hours of investment, and all were built with HTML and JavaScript and run in the browser; he suggests starting with prompts like "Build a
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Hugging Face Shares Industry Practices for Training Agents: Environments, Evaluation, and Rewards Are Harder Than the Model
Hugging Face released four talks, a panel discussion, and a Q&A, discussing practical approaches to training Agents in industry, with the core conclusion being that environment, evaluation, and reward design are harder than the model itself. Miles used 64×B300 fully asynchronous training on Terminal Bench 2, and after about 100 steps the reward rose from about 0.44 to 0.56; OpenEnv Arena, after filtering down layer by layer from about 90,000 findable RL environments, had only about 800 relatively clean and usable ones left. The talks also mentioned cheating problems such as Agents finding reference answers in the sandbox and modifying the scoring logic to fake a pass, as well as about 1% of cheating problems contaminating an entire round of training.
Hugging Face released four talks, a panel discussion, and a Q&A, discussing practical approaches to training Agents in industry, with the core conclusion being that environment, evaluation, and reward design are harder than the model itself. Miles used 64×B300 fully asynchronous training on Terminal Bench 2, and after about 100 steps the reward rose from about 0.44 to 0.56; OpenEnv Arena, after filtering down layer by layer from about 90,000 findable RL environments, had only about 800 relativel
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Claude engineering lead Felix on how to use Claude: fewer role-based Agents, use adversarial review to constrain output
In an interview, Claude engineering lead Felix Rieseberg said he barely writes code anymore; his core approach is to hand crash data, business tools, and a computer to the model and let it decide what to fix itself, rather than having a human act as a relay repeating things item by item. He suggests opening multiple threads on the same output dedicated to proving it wrong, then opening another as a judge to filter which criticisms hold up; rather than giving each Agent a tiny slice of data, it is better to give a panoramic business view and well-documented tools. He also mentioned that the half-life of Skills is extremely short, that both official guidance and his own advice are to use them cautiously, and that self-evaluation within the same session remains unreliable, so external signals should be sought as much as possible.
In an interview, Claude engineering lead Felix Rieseberg said he barely writes code anymore; his core approach is to hand crash data, business tools, and a computer to the model and let it decide what to fix itself, rather than having a human act as a relay repeating things item by item. He suggests opening multiple threads on the same output dedicated to proving it wrong, then opening another as a judge to filter which criticisms hold up; rather than giving each Agent a tiny slice of data, it i
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