Fresh stories
Codex updates app with customizable shortcuts and 10-50x faster Git ops
OpenAI shipped shortcut customization, restored Git controls, cleaned up panels, and sped up large-repo operations in Codex. Paid-plan usage caps were also reset, though some accounts saw delayed propagation.

SGLang 0.5.12 adds DeepSeek V4 serving with ShadowRadix and HiSparse
SGLang v0.5.12 added native DeepSeek V4 support with ShadowRadix prefix caching, HiSparse CPU-extended KV, MegaMoE kernels, and Blackwell MLA work. The release broadens hardware targets and improves long-context serving efficiency for open runtimes.

Zero launches systems language for agents after 3,000 agent tasks
Triangle Company introduced Zero as a systems language aimed at agent-friendly tooling and said the compiler mostly self-hosts after about 3,000 agent tasks in three days. Early inspection praised the tiny C compiler but found broken Mach-O lowering and no fuzz tests, so the release looks experimental rather than production-ready.


Codex updates app with customizable shortcuts and 10-50x faster Git ops
OpenAI shipped shortcut customization, restored Git controls, cleaned up panels, and sped up large-repo operations in Codex. Paid-plan usage caps were also reset, though some accounts saw delayed propagation.

Codex adds remote connections for Mac mini devboxes in the ChatGPT app
OpenAI documented Codex remote connections, letting the ChatGPT app point at a separate Codex host such as a Mac mini or rented VPS. Try it for long runs that need to stay alive off-device or for phone-first coding sessions.

Codex users report 2-hour mech-interp runs and 150-hour tasks with `/goal`
Days after `/goal` workflows first surfaced, users showed the command also works in the Codex app and shared runs for SSH setup, mech-interp scripts, and recurring work that lasted hours or days. The evidence points to Codex being used as a long-running research and ops agent, though the app still lacks explicit `/goal` UI.

SGLang 0.5.12 adds DeepSeek V4 serving with ShadowRadix and HiSparse
SGLang v0.5.12 added native DeepSeek V4 support with ShadowRadix prefix caching, HiSparse CPU-extended KV, MegaMoE kernels, and Blackwell MLA work. The release broadens hardware targets and improves long-context serving efficiency for open runtimes.
Claude Code users report tmux claude-p wrappers and cache fixes after June 15
llama.cpp provider adds in-process AI SDK support with tool calling
Mythos benchmarks 69% on ExploitBench with 16 T1 envs vs GPT-5.5 Codex's 2
Zero launches systems language for agents after 3,000 agent tasks
Top storiesthis week
Claude Code users report metered -p mode and slower headless sessions after credit split
A day after developers flagged Anthropic’s SDK credit split, Claude Code users said -p work had become metered, slower, and harder to run headlessly. Anthropic reset 5-hour and weekly limits, and Claude Code 2.1.143 added projected context-cost estimates.


OpenAI fixes two GPT-5.5 issues in Codex after users report looping runs
OpenAI said Codex’s GPT-5.5 degradation over the prior 48 hours came from two issues and it will reset usage limits after the fix. Users had reported looping runs, higher cache burn, and unstable sessions in active coding workflows.

OpenClaw users report Hermes Agent migrations with clearer approvals, cron jobs, and Telegram UX
Practitioners said skills and workflows were porting from OpenClaw to Hermes Agent with fewer surprises around approvals, job control, and mobile use. That matters because teams choosing a self-hosted agent stack are now comparing operational clarity and migration friction, not just model support.

OpenClaw ships 3.5x RTT tests and Clawpatch guardrails for coding agents
OpenClaw added end-to-end RTT tests and new auditable guardrails while community builders shipped Clawpatch, credential brokers, and ARC harnesses. The stack now has clearer safety and benchmarking primitives for long-lived coding agents.

Nous Research releases Lighthouse Attention: 1.4-1.7x faster pretraining at 98K context
Nous Research published Lighthouse Attention, a hierarchical selection layer that keeps the standard attention kernel while cutting end-to-end pretraining wall clock by 1.4-1.7x at 98K context. It also scales to 1M-token training across 32 Blackwell GPUs without a custom sparse kernel.




