Software engineering isn't dying. It's changing shape.
I've been thinking about this a lot with the layoffs and "AI is replacing devs" takes. The job isn't going away, but the skills that matter are shifting.
I wrote about the five I think matter most.
https://t.co/5hG2iemJr9
When AI makes coding fast, the bottleneck shifts from implementation to coordination. I wrote about what my team changed: parallel workstreams, specs as the coordination layer, and why we're spending more time in design reviews, not less.
https://t.co/ZKWvMdzKA3
Phase shift in engineering is the right frame. The bottlenecks that aren’t addressed yet are human coordination and trust. That's the next loop to close.
Caught up with @karpathy for a new @NoPriorsPod: on the phase shift in engineering, AI psychosis, claws, AutoResearch, the opportunity for a SETI-at-Home like movement in AI, the model landscape, and second order effects
02:55 - What Capability Limits Remain?
06:15 - What Mastery of Coding Agents Looks Like
11:16 - Second Order Effects of Coding Agents
15:51 - Why AutoResearch
22:45 - Relevant Skills in the AI Era
28:25 - Model Speciation
32:30 - Collaboration Surfaces for Humans and AI
37:28 - Analysis of Jobs Market Data
48:25 - Open vs. Closed Source Models
53:51 - Autonomous Robotics and Atoms
1:00:59 - MicroGPT and Agentic Education
1:05:40 - End Thoughts
Keeping up with new AI dev tools is like drinking from a firehose. But while tools change constantly, the patterns are much more stable. I wrote up the 4 durable patterns I use for AI-assisted development to get things done without overcomplicating it.
https://t.co/KB0GehMt4t
Cursor is raising at a $50 billion valuation on the claim that its “in-house models generate more code than almost any other LLMs in the world.” Less than 24 hours after launching Composer 2, a developer found the model ID in the API response: kimi-k2p5-rl-0317-s515-fast.
That’s Moonshot AI’s Kimi K2.5 with reinforcement learning appended. A developer named Fynn was testing Cursor’s OpenAI-compatible base URL when the identifier leaked through the response headers. Moonshot’s head of pretraining, Yulun Du, confirmed on X that the tokenizer is identical to Kimi’s and questioned Cursor’s license compliance. Two other Moonshot employees posted confirmations. All three posts have since been deleted.
This is the second time. When Cursor launched Composer 1 in October 2025, users across multiple countries reported the model spontaneously switching its inner monologue to Chinese mid-session. Kenneth Auchenberg, a partner at Alley Corp, posted a screenshot calling it a smoking gun. KR-Asia and 36Kr confirmed both Cursor and Windsurf were running fine-tuned Chinese open-weight models underneath. Cursor never disclosed what Composer 1 was built on. They shipped Composer 1.5 in February and moved on.
The pattern: take a Chinese open-weight model, run RL on coding tasks, ship it as a proprietary breakthrough, publish a cost-performance chart comparing yourself against Opus 4.6 and GPT-5.4 without disclosing that your base model was free, then raise another round.
That chart from the Composer 2 announcement deserves its own paragraph. Cursor plotted Composer 2 against frontier models on a price-vs-quality axis to argue they’d hit a superior tradeoff. What the chart doesn’t show is that Anthropic and OpenAI trained their models from scratch. Cursor took an open-weight model that Moonshot spent hundreds of millions developing, ran RL on top, and presented the output as evidence of in-house research. That’s margin arbitrage on someone else’s R&D dressed up as a benchmark slide.
The license makes this more than an attribution oversight. Kimi K2.5 ships under a Modified MIT License with one clause designed for exactly this scenario: if your product exceeds $20 million in monthly revenue, you must prominently display “Kimi K2.5” on the user interface. Cursor’s ARR crossed $2 billion in February. That’s roughly $167 million per month, 8x the threshold. The clause covers derivative works explicitly.
Cursor is valued at $29.3 billion and raising at $50 billion. Moonshot’s last reported valuation was $4.3 billion. The company worth 12x more took the smaller company’s model and shipped it as proprietary technology to justify a valuation built on the frontier lab narrative.
Three Composer releases in five months. Composer 1 caught speaking Chinese. Composer 2 caught with a Kimi model ID in the API. A P0 incident this year. And a benchmark chart that compares an RL fine-tune against models requiring billions in training compute without disclosing the base was free.
The question for investors in the $50 billion round: what exactly are you buying? A VS Code fork with strong distribution, or a frontier research lab? The model ID in the API answers that.
If Moonshot doesn’t enforce this license against a company generating $2 billion annually from a derivative of their model, the attribution clause becomes decoration for every future open-weight release. Every AI lab watching this is running the same math: why open-source your model if companies with better distribution can strip attribution, call it proprietary, and raise at 12x your valuation?
kimi-k2p5-rl-0317-s515-fast is the most expensive model ID leak in the history of AI licensing.
Both A2A and UCP independently converged on the /.well-known/ URL discovery pattern organically. That's how many REST patterns emerged too. When protocols converge without coordination, it usually means the underlying shape is right.
https://t.co/9HvCVgRdpo
Checkout this OSS tool for creating and running multi-agent workflows with the GitHub Copilot SDK and Anthropic Agents SDK. You can even use different providers in the same workflow 💪
https://t.co/yXoGhVFiQB
100000%, yes! Peer-to-peer wins beat top-down mandates every time!
"""The most successful tactic has been sharing wins. When engineers see examples from their peers where AI helped them accomplish something impressive, adoption spreads quickly."""
https://t.co/O0wTh68jOX
Anthropic's 1M context going GA with flat pricing is less about the ceiling and more about removing the floor tax.
I've been using it since it was available in beta and its allowed me to stop thinking so much on context limits and start focusing more on the problems.
Introducing Claude Opus 4.6. Our smartest model got an upgrade.
Opus 4.6 plans more carefully, sustains agentic tasks for longer, operates reliably in massive codebases, and catches its own mistakes.
It’s also our first Opus-class model with 1M token context in beta.
This is pretty wild and the scariest part wasn't the 46.5M chat messages exposed. It was write access to the system prompts governing over 40k consultants' AI. The prompt layer is the new crown jewel. https://t.co/oaitL7zcJF