AI coding

Latent Space says Vlad Feinberg’s pretraining job-prep notes reduce frontier-lab readiness to kernel-level performance work: derive Chinchilla laws, compare dense and MoE architectures, code the solution in JAX, then write a Pallas kernel that beats jax.lax.ragged_dot for F > D by fusing up/down projections.

The Information says 34 leading AI startups reached about $80 billion in annualized revenue, with OpenAI and Anthropic taking 89%, while Anthropic exceeded $30 billion in April 2026 and surpassed OpenAI’s reported $25 billion.

Jiqizhixin translated Sebastian Raschka’s blog on recent LLM architecture changes, covering long-context cost reductions in Gemma 4, Laguna XS.2, and ZAYA1-8B; the article states that Gemma 4 E2B saves about 2.7GB of KV cache at 128K context with bfloat16 precision.

Cursor released Composer 2.5, claiming a 10x efficiency gain at comparable capability, with larger training scale, more complex reinforcement-learning environments, and a text-feedback mechanism.

Anthropic acquired Stainless, a New York startup founded in 2022 that automates creation and maintenance of SDKs for developers using APIs; the post does not disclose the deal price or Anthropic’s integration plan.

Claude Code fast mode now defaults to Opus 4.7, and the post discloses the /fast invocation method but does not disclose pricing, context window, rate limits, or rollout conditions.

llama.cpp merged MTP speculative decoding in PR #22673; Qwen3.6 27B Q8_0 rose from 7.4 to 18.1 tok/s on Strix Halo, while a dual RTX 3090 Q8_0 setup rose from 25.7 to 55.9 tok/s.

Cursor released Composer 2.5, claiming up to 10x higher efficiency on long coding tasks; the model is further trained on Moonshot’s Kimi K2.5 and uses text feedback for 100k-token-scale trajectories.

GitHub launched remote control sessions for Copilot, letting users start tasks in VS Code or the command line and continue them through github.com or GitHub Mobile.

InsForge released an Apache 2.0 backend platform that lets coding agents deploy, operate, and debug backend systems through one CLI install command and Skills.

The author tested Qwen 3.6 27B on an RTX 3090 24GB and kept ik_llama.cpp with Qwen3.6-27B-MTP-IQ4_KS.gguf; at 156k context with q8_0 KV and MTP, a ~5.9k-token prompt plus 1024-token output reached about 1261 tok/s prefill and 72.9 tok/s decode, while vLLM lacked a clean single-card long-context run.

OpenAI and Dell are partnering to bring the Codex coding agent to enterprise hybrid-cloud and on-premises deployments; the RSS snippet does not disclose launch timing, pricing, supported regions, or the specific security controls for sensitive data.

SmallCode passes 87 of 100 benchmark tasks with Gemma 4 activating 4B parameters per token. The author attributes the result to compound tools, compile and lint feedback, task decomposition after two repeated failures, and optional escalation to Claude or OpenAI for one task.

The team applied Mythos and other security-focused LLMs to real-time code testing for critical infrastructure; the post reports vulnerability detection strengths, false positives, and unstable context handling, but does not disclose sample size or benchmark metrics.

Tencent Cloud opened public beta for Ardot, an AI design agent that generates editable app pages, websites, and posters from one-sentence prompts, then converts designs to code.

Cursor released Composer 2.5, built on a Moonshot open-source checkpoint, trained with synthetic data from real codebases at 25 times the previous scale, and updated with text-feedback reinforcement learning and a sharded Muon optimizer.

MinishLab open-sourced Semble, a code-search tool for agents that combines Model2Vec embeddings, BM25, RRF fusion, and reranking; on a 63-repo benchmark, it used 98% fewer tokens than grep+read, reached 0.854 NDCG@10, and ran CPU queries in about 1.5 ms.

A Reddit user tested DeepSeek V4 on 45k, 180k, and 520k-token codebases and found 150k-250k tokens best for coding work. Past 300k tokens, line-number precision degraded; at 520k, outputs shifted toward architecture summaries and skipped implementation details.

Researchers used OpenHands to analyze traces from 8 frontier models on 500 swe-bench-verified tasks, finding that agentic coding reached a 154:1 input-output token ratio and that human difficulty labels correlated weakly with token use at Kendall tau 0.32.

Peter Steinberger used 603 billion tokens across 7.6 million requests in 30 days, with the bill exceeding $1.3 million; he said disabling fast mode cut the price by 70%, and OpenAI does not charge him for the tokens.