AI coding agents are powerful… but chaotic. Archon + Agent Skills turn them into deterministic PR machines. Parallel agents. Zero merge conflicts. Running locally on my M4 Pro.

The AI coding market seems to be shifting quickly. Recent data from 100+ top AI-natve devs on @CostHawkAI is worth paying attention to. Codex usage is now accelerating much faster than Claude Code. Codex: +180.6% MoM Claude Code: 86.7% market share (Leading, but losing momentum fast) The gap is closing in real-time. Live Leaderboard:

Poe now supports @opencode An open-source AI coding terminal that pairs with all major models on Poe. One click login, instant access, no extra configuration. Start building now.

Should there be a Stack Overflow for AI coding agents to share learnings with each other? Last week I announced Context Hub (chub), an open CLI tool that gives coding agents up-to-date API documentation. Since then, our GitHub repo has gained over 6K stars, and we've scaled from under 100 to over 1000 API documents, thanks to community contributions and a new agentic document writer. Thank you to everyone supporting Context Hub! OpenClaw and Moltbook showed that agents can use social media built for them to share information. In our new chub release, agents can share feedback on documentation — what worked, what didn't, what's missing. This feedback helps refine the docs for everyone, with safeguards for privacy and security. We're still early in building this out. You can find details and configuration options in the GitHub repo. Install chub as follows, and prompt your coding agent to use it: npm install -g @aisuite/chub GitHub:

The WTR AI Private Pools Index climbed another 14.8% M/M through May 11, pushing total tracked private AI market value to $2.20 trillion across 38 active constituents. 🔹 Anthropic drove the move higher, adding +$276.8B in value to reach a ~$900.5B valuation, while OpenAI remained the second-largest constituent at ~$839.0B 🔹 The index is now up 64.1% YTD, underscoring continued investor appetite for AI infrastructure, foundation models, and enterprise AI platforms 🔹 Harvey, Figure AI, and ElevenLabs were among the largest monthly gainers, while Cursor saw pressure amidst competition from Anthropic’s Claude Code and other AI coding tools Read James Kisner, CFA's full report for more detail on private AI valuation trends, index composition, top movers, and secondary market activity shaping the AI ecosystem. #AI# #ArtificialIntelligence# #PrivateMarkets# #Anthropic# #OpenAI# #GenerativeAI# #MachineLearning# #Technology# #Investing# #WaterTowerResearch#

Step into a 1,000m² AI universe. Registration for Qwen Conference 2026 is officially open! Join us at Singapore’s Sands Expo on May 26 to experience the New Era in person. From foundation models to hands-on AI coding—this is your journey from code to impact. 🔗 Link in bio to secure your pass. See you in Singapore! #AlibabaCloud# #QwenConference2026# #Qwen# #LLM#

Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research. Frontend development — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast! Backend development — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents. Infrastructure. Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development. Research. Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally. Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much. I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future posts. [Original text: ]