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Simon Willison's Weblog·2 months ago·
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Tencent released Hy3, a 295-billion-parameter mixture-of-experts model under Apache 2.0 license, following feedback from 50+ products that used its April preview version. The model uses 21 billion active parameters and supports a 256,000-token context length, with the full version requiring 598GB storage and a quantized version at 300GB. The model is available free on OpenRouter until July 21st and competes with open-source models having 2-5 times more parameters.
OpenAI CEO Sam Altman is in talks with President Trump about giving the US government a 5% stake in the company, part of a broader plan to distribute AI wealth to Americans. A 5% stake in OpenAI, valued at $852 billion after its March funding round, would equal approximately $42.6 billion, or roughly $320 per American household if distributed equally. The proposal remains largely theoretical after five years of discussion, but could help OpenAI secure favorable treatment from the Trump administration while addressing public concerns about AI companies' accountability.
The Algorithmic Bridge·2 months ago·
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A newsletter writer explains that organizations waste money on AI tools by paying for tokens they don't efficiently use, citing examples like Uber's $1,500 monthly engineer budget cap and Tesla's $200 weekly limit after overspending. The article promises four strategies to improve value-per-dollar spent on AI, including selecting appropriate models for specific tasks and avoiding unnecessary token consumption. Better optimization practices could reduce AI costs while maintaining output quality instead of alternating between excessive spending and severe budget cuts.
Small AI models that run on low-power devices without internet connections are gaining adoption in developing countries for applications like counterfeit drug detection, crop disease identification, and malaria mosquito detection, with advocates arguing they are more sustainable and accessible than large language models. By the end of 2026, more than half of all smartphones worldwide will be capable of running small AI models, driven by improvements in hardware and the release of open-weight models like Google DeepMind's Gemma 4 and Alibaba's Qwen 3.5. The shift toward small AI deployment could reach millions of people without access to data centers or reliable electricity, though long-term success depends on countries investing in supporting infrastructure like power grids and digital supply chains.
Open AI models from NVIDIA and partners have become central to research presented at ICML 2026, with approximately 2,000 accepted papers citing NVIDIA GPUs and hundreds more building on specific open model families like Nemotron and Cosmos. Key applications include robot world models like DreamDojo for physical AI reasoning, protein prediction models like FLIP2 and KERMT for drug discovery, and synthetic data generation tools enabling large-scale training without human labeling. Companies including Merck, Sakana AI, and robotics manufacturers are deploying these open foundations to accelerate development in autonomous vehicles, biomedical research, and humanoid robotics.
Photoroom built PRX's training dataset by mixing public and internal sources, then re-captioning all images with a vision-language model for consistency before converting everything to a standardized format. The team stored 100 million+ images in Lance format for exploration and filtering, then streamed them via Mosaic Data Shards during training, with text latents computed on-the-fly at a 3-4% throughput cost. This approach prioritized dataset breadth over individual image perfection, allowing the model to learn diverse visual concepts while keeping storage and compute efficient.
Anthropic pursues advanced AI development alongside safety research, positioning itself as an influential voice in AI governance. The company's strategy rests on building cutting-edge systems to gain authority in discussions about AI risks and safeguards. By maintaining technical leadership, Anthropic aims to shape industry standards and policy decisions around AI safety.
Countries are building domestic AI capabilities through national strategies focused on local infrastructure, data, and workforce to develop AI solutions tailored to their citizens and regulations. Nations are investing in AI factories—next-generation data centers for advanced computing—with examples including France automating public service workflows and India creating multilingual models for its 22 official languages. These localized AI ecosystems enable countries to strengthen economic growth, national security and innovation while maintaining control over their data and governance.
Nvidia's Grace-Blackwell GPU deployment remains slow with over 95% of units not yet deployed since December 2024. An AI model with 35 billion parameters now matches trillion-parameter models on long-horizon benchmarks using a different training approach. These advances suggest smaller, more efficient models may compete with larger systems across different tasks.
Fable, an AI system, created a GPU kernel achieving 18.71X speedup over optimized PyTorch baselines on the KernelBench-Mega benchmark, outperforming Claude Opus at 14.4X and other models. This represents progress toward autonomous AI development of fundamental computing infrastructure, potentially enabling recursive self-improvement in AI systems. As AI systems become better at coding and optimization tasks essential to AI research, they accelerate the capability expansion cycle that determines economic competitiveness.
A developer reverse-engineered Cognition's Security Swarm architecture to create a generalized Agentic MapReduce system that coordinates multiple AI agents across large codebases using git branches and commits as the communication primitive. Testing on 13 security audit repositories with 3 iterations each cost $26.70 per run on average, with 6 of 13 finding target vulnerabilities and 5 others surfacing confirmed CVEs. The approach enables fault tolerance, auditability, and scalability for distributed agent tasks without task-specific infrastructure, though the developer is building Corigin as a specialized git platform for agents since GitHub wasn't designed for machine-generated branch management.
James Obermayer published a detailed technical guide for building local GPU systems to run state-of-the-art language models, ranging from $2,000 setups with RTX 3090s running Qwen models to $40,000+ configurations with 4× RTX 6000 Pro cards (384GB VRAM) running models approaching Claude Opus quality. The guide includes specific hardware bill-of-materials, BIOS configuration steps, kernel parameters, and Docker-based serving configurations, with the $40k system achieving 27.5 GB/s unidirectional GPU peer-to-peer bandwidth through custom PCIe Gen4 switches. Users can now deploy high-performance local inference with detailed hardware recommendations and ready-to-run software configurations instead of relying on cloud APIs.
AMD's MI355X GPU achieves comparable inference performance to NVIDIA's Blackwell at roughly 2.75x lower cost per unit, though it historically lagged due to software support issues. Wafer demonstrated 2626 tokens per second aggregate throughput on the MI355X versus 3192 on Blackwell, and 213 tokens per second on GLM5.2, by optimizing quantization, speculative decoding, and kernel selection. As software tooling and agent-driven optimization improve, AMD's cost advantage is becoming increasingly accessible without requiring custom kernel development.
Thirty-five CLI coding agents existed as of July 2026, with the field shifting from IDE-based tools toward terminal-based systems that work without graphical interfaces. Google's Gemini CLI reached 106k GitHub stars before Google killed its free tier on June 18, 2026, forcing users to its closed-source replacement Antigravity CLI. The standardization around Model Context Protocol and open-weight models like GLM-5.2 and Qwen3-Coder has lowered barriers to entry, enabling more vendors and reducing subscription costs relative to frontier capability.
Plannotator is a local browser-based tool for reviewing and annotating AI agent work, including plans, code, and documents, before implementation. The tool integrates with multiple AI coding agents (Claude Code, Copilot, Gemini CLI, and others) through hooks and commands, allowing users to mark up proposals and send feedback back to agents for revision. Users can review code diffs, pull requests, markdown files, and HTML artifacts, with features including plan mode automation, code review with git integration, and optional team collaboration through encrypted URL sharing.
A software developer at Prime Radiant describes new patterns for building AI agents, particularly focusing on using agents directly in development loops rather than traditional human feedback. The architecture uses credential compartmentalization with ephemeral subagents, MITM proxies, and arbiter agents to manage security risks. The key innovation is having Claude Code collaborate with another agent (Ada-sen) on iterative development, where Claude proposes changes, Ada tests them, and they iterate together without human intervention in the loop.
A developer argues that when using AI coding agents, writing code directly rather than detailed specifications leads to better maintainability and quality control. The approach involves establishing patterns through manual coding, then gradually expanding what the agent handles while maintaining human oversight through tests and code review. This method preserves developer understanding of the system and prevents the brittleness that comes from trying to maintain both specs and code.
A researcher conducted extensive experiments to test whether URLs in prompts influence LLM outputs based on their content or the URL string itself, spending significant API costs running tests across multiple models. The key finding is that URLs influence output only when their content was present in the model's training data, with server-rendered content averaging 55% recall from bare URLs while client-rendered content averaged 6% recall, and famous identifiers like arxiv.org/abs/1706.03762 decoding reliably due to memorization during training. Training data transparency is needed from LLM providers, as crawlers like ClaudeBot and GPTBot do not execute JavaScript, meaning single-page applications are effectively invisible to models despite being crawlable in their rendered form.
An engineer describes their experience using AI agents for coding tasks, including instances where the AI fabricated results while appearing credible, and argues that AI-assisted development should rely heavily on automated testing rather than code review, drawing parallels to CPU design practices from a hardware company that achieved high reliability through fuzzing and property-based testing instead of human review.
Newer Anthropic Claude models (Opus 4.8 and Sonnet 5) sometimes fail to properly call external tools by inventing extra fields in the API schema, whereas older models did not exhibit this problem. The failure rate reaches approximately 20% in some agentic contexts and can be reduced to zero by enabling strict tool invocation mode. This regression appears to result from post-training on Claude Code's forgiving tool harness, which silently repairs malformed calls and accepts parameter aliases, causing newer models to learn looser compliance with unfamiliar tool schemas.
Junior programmer employment has declined 19% since late 2022 while older developers thrived, with entry-level software postings down 28%, but simultaneously a surge of 36 million new GitHub accounts and 80% year-over-year growth in iOS app submissions shows that coding capability is spreading to non-programmers in other job roles. The collapse in traditional junior programming jobs contrasts with an explosion in people writing software outside traditional developer titles, as marketing managers and product managers now use AI coding tools. The profession faces a critical gap: the apprenticeship model that trained senior engineers is broken, creating a potential shortage of experienced developers unless companies redesign junior roles around specification and customer contact rather than code production.
Claude users should spend more time identifying and defining unknowns before executing long-horizon tasks to avoid costly failures. The article recommends using Claude through explainers, brainstorms, interviews, prototypes, and references to clarify these unknowns early. This upfront definitional work prevents problems from becoming expensive later in project execution.
A framework for agentic AI systems categorizes autonomy levels from low (limiting risk and reversibility) to high (better for explicit tasks and parallel agent fleets). The frontier approach uses a manager agent that delegates to helper agents while verifying outputs and escalating only decisions requiring human judgment. This represents a design pattern for balancing AI autonomy with human oversight in multi-agent systems.
AI systems designed for forecasting, known as AI superforecasters, are now competitive with or slightly better than top human forecasters on prediction markets and financial tasks. One startup claimed to turn $35 into $2 million on Kalshi over seven months, and Metaculus data shows scaffolded AI systems are roughly nine months ahead of base models, with human-AI parity expected around six months away. If AI forecasters match human experts in accessibility and cost while maintaining comparable accuracy, they could democratize access to sophisticated predictions currently available only through expensive expert consultants.
Nvidia launched a partnership program connecting AI startups with cloud service providers to access computing infrastructure powered by Nvidia chips, with revenue sharing between the parties. The company named Sharon AI and Firmus Technologies as initial partners, with the latter building a data center expected to reach 170,000 Nvidia GPUs. The move addresses startup demand for scarce GPU capacity as the sector faces liquidity constraints and compute availability issues.
China's web novel platforms, which initially promoted AI writing tools to boost author productivity, are now cracking down on AI-generated content as readers complain about quality and detect telltale signs of machine writing. In June 2024, ByteDance's Tomato Novel rejected over 104,000 low-quality submissions including AI-written stories, while Jinjiang's founder instructed authors to use AI only for research and proofreading. The platforms face pressure to balance AI's potential to democratize storytelling against threats to authenticity and reader engagement, with subscription-based sites like Jinjiang taking stricter stances than free, ad-supported platforms like Tomato Novel.
Deptrust is a CLI tool that checks package versions across 13 major package ecosystems (npm, PyPI, Cargo, Go modules, RubyGems, NuGet, Maven, Packagist, pub.dev, CocoaPods, Hex.pm, Hackage, and GitHub Actions) for known vulnerabilities by querying public registries and OSV APIs directly. The tool queries multiple vulnerability providers in parallel, including OSV and GitHub Advisory Database, and returns a recommendation ranging from "block" for critical vulnerabilities to "allow" when no known issues are found. Deptrust integrates with AI agents like Codex and Claude Code via MCP servers or CLI skills to automatically prevent agents from installing vulnerable package versions before they execute install or update commands.
A Python framework called claude-real-video lets AI models process videos by extracting only the frames that change, rather than sampling at fixed intervals, with local processing that keeps the source video on your machine. The free version detects scene changes and removes duplicates to reduce frame count from 58 to 26 in a sample clip; the paid Pro version adds cinematography analysis and timestamps for non-visual elements like gestures and sound, priced at $19 through July 31 then $29 afterward. Users can now feed videos to Claude, ChatGPT, or Gemini with fewer, more meaningful frames and a transcript, reducing token costs while improving understanding compared to fixed-interval sampling or transcript-only approaches.
Moonshot AI released Kimi Code, a command-line coding assistant integrated with their K2.7 model that handles software development, codebase analysis, and technical tasks. The tool installs via a single bash command and operates as a CLI interface within Kimi's membership subscription plans. Users can now perform coding work directly from their terminal without switching between development environments and a separate AI tool.
Eve is a filesystem-first framework for building AI agents where core capabilities are organized in conventional directory structures to simplify inspection and extension. A new agent is created with `npx eve@latest init my-agent`, which generates a project scaffold with folders for instructions, tools, skills, channels, and schedules. The framework allows developers to define agent behavior through markdown files and TypeScript functions stored directly in the filesystem rather than through configuration APIs.
Z.ai released ZCode, a free coding assistant powered by the GLM-5.2 model, designed to compete with commercial alternatives like Cursor and GitHub Copilot. The tool is available at no cost to users, contrasting with paid offerings from competitors. This expands the free coding assistance market, potentially reducing adoption barriers for developers choosing between AI code editors.
Kling AI, Kuaishou's video generation unit, raised $2 billion in venture funding as the company prepares to spin off ahead of a planned 2027 IPO. The pre-money valuation was approximately $15 billion, with potential additional investors potentially pushing the total to $3 billion and diluting Kuaishou's stake to 68 percent. Kling now competes directly with ByteDance's Sensescape and other startups to fill the market gap left by OpenAI's shutdown of Sora, positioning itself as the dominant text-to-video platform for professional creators.
Anthropic's relationship with the Pentagon deteriorated over disagreements about safety guardrails, data access, and restrictions on military applications of its AI systems. The conflict centered on Anthropic's unwillingness to remove safeguards that the Defense Department wanted modified or eliminated for operational use. The breakdown resulted in reduced collaboration and highlighted tension between Anthropic's stated safety priorities and U.S. military acquisition goals.
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Zoom has agreed to acquire Common Room, an AI platform that consolidates buyer intelligence and sales signals from fragmented sources like CRM and product usage data. The deal is expected to close in the coming weeks, with financial terms undisclosed. Common Room's AI agents and buyer intelligence capabilities will integrate with Zoom's existing Revenue Accelerator platform to provide sales teams unified access to account research, personalization, and prospecting tools within their existing workflows.
Amazon is designing custom semiconductors for its Echo and Fire TV devices to run AI models locally rather than relying on cloud processing. The company unveiled its AZ3 and AZ3 Pro chips in October, which handle on-device AI inference for improved speed and security. Amazon plans to expand this approach across additional consumer devices and is developing portable AI gadgets that will sync contextual data across its ecosystem of products.
Cloudflare introduced a new classification system for AI crawlers that divides them into three categories—Search, Agent, and Training—allowing website owners to manage different types of automated traffic separately rather than blocking all AI bots at once. Starting September 15, 2026, new domains will have Training and Agent crawlers blocked by default on ad-supported pages, while Search crawlers remain allowed. Website owners can now grant different permissions based on how bots use content, ranging from immediate interaction-only access to full reproduction rights, giving them finer control over their content's use in AI systems.
WeWeb MCP lets non-coders use an MCP AI agent to generate app pages, workflows, data models, auth, and integrations inside WeWeb’s visual editor for review and edits. The article says this is WeWeb.io’s 4th launch. As a result, teams can build production-ready apps using an AI “agent” while keeping control through the no-code interface and optionally exporting code for deployment elsewhere.
Hugging Face introduced a new "kernel" repository type on its Hub with enhanced security features including trusted publisher verification and code signing to prevent malicious kernel execution. The project now supports multiple frameworks beyond PyTorch, including Apache TVM FFI, and has restructured its CLI tools to separate concerns between kernels (for loading) and kernel-builder (for building). These changes establish infrastructure for agentic kernel development, enabling AI agents to automatically scaffold, build, benchmark, and optimize kernels across different hardware configurations.
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