A solo dev RL-trained a 4B model into a query planner that beats Postgres by 81% — small models are eating heuristics.
Top Signal
A 4B model RL-trained to out-plan the Postgres query optimizer
research to practice
HN Front Page
Rohan Bansal trained a 4B model with RL to emit query plans that execute 81% faster than what Postgres's cost-based optimizer picks. The important part isn't the number, it's the recipe: take a narrow systems task where a hand-tuned heuristic has been 'good enough' for 30 years, define a hard reward signal you can actually measure (execution time), and fine-tune a small open model until it beats the heuristic. No frontier model, no API bill, no agent scaffolding. Query planning is unusually well-suited — the reward is objective, cheap to compute, and infinitely sampleable from your own workload. What to do: read the writeup for the reward shaping and plan-serialization details, then audit your own stack for tasks with the same shape (cache eviction, retry/backoff policy, index selection, job scheduling, autoscaling). If you can measure it in milliseconds, you can probably RL a 4B model into beating your heuristic.
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Fast Signals
Offload most of Qwen3.8-Flash-Next's KV cache to RAM, keep decode speed
workflow
r/LocalLLaMA
A r/LocalLLaMA user shows you can push the bulk of the KV cache to system RAM with minimal decode slowdown, letting you run a quant that barely fits in VRAM at full context length without KV quantization. The trick should generalize to anything on the qwen4exp base, which Qwen's next local models will use. If you've been quantizing KV to buy context, try this first — you get the context back without the quality tax.
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New paper breaks the 1.58-bit barrier for ternary LLMs
research to practice
HN Front Page
BitNet-style ternary weights have been treated as the floor at 1.58 bits per parameter; this arXiv paper pushes below it. Matters if you're sizing local deployments — every fraction of a bit compounds across billions of params, and sub-ternary is where 30B-class models start fitting on consumer cards without the usual quality cliff. Bookmark for your next hardware-constrained deployment decision; no runnable artifacts yet.
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Xiaomi streams MiMo 2.6's RL post-training run live
emerging signal
HN Front Page, r/LocalLLaMA
Xiaomi put up a public dashboard showing MiMo 2.6's post-training in progress — reward curves, eval checkpoints, the works. It hit both HN and r/LocalLLaMA within hours, which is the convergence tell. Nobody publishes RL curves mid-run; if you're doing any RLHF/RLVR work, this is free calibration on what a healthy reward trajectory actually looks like at scale.
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Anthropic collapses Cowork and chat into one Claude
platform change
Simon Willison
The Cowork vs. Claude vs. Claude Code split is gone — Cowork and chat are now a single product. Practical effect: one surface, one set of affordances to explain to your team, and one fewer axis of confusion when you document internal workflows. Check any onboarding docs or internal runbooks that reference Cowork as a separate thing.
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Willison ships a browser tool for the Gemini 3.8 Live audio API
new tool
Simon Willison
Simon Willison built a small hosted tool that exercises Gemini 3.8 Live and Live Extended Thinking from the browser. Useful as a working reference implementation for the realtime audio path — read the source before you write your own WebSocket plumbing. Fastest way to feel out latency and interruption behavior before committing to the API.
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63 hours, 50M tokens: a local Qwen run as long-horizon agent telemetry
workflow
r/LocalLLaMA
Someone ran Qwen 3.8 27B (4-bit, 100K context) autonomously for 63 hours against the Riemann hypothesis. It didn't solve it — the value is the trace: how the model organized memory, chose strategies, and degraded over 50M+ tokens on a single 3090. Rare public data on what actually breaks in very long autonomous loops. Read it before you design your next agent's memory layer.
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Radar
Droid ASC: Android decompiler built for agents, not humans
Turns R8 compiler optimization into a decompiler primitive, with output shaped for agent consumption rather than human reading. A concrete example of the 'retool existing analysis pipelines for machine readers' pattern worth stealing in your own domain.
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stale.jock.pl tracks release age and training cutoff for 20 models
A single page listing how old each major model is and where its knowledge stops. Bookmark it for the recurring argument about why your model doesn't know about a library released last quarter.
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Dream-RSI: self-improvement via procedurally evolving worlds
Proposes recursive self-improvement where the environment itself evolves alongside the agent, rather than fixing a benchmark and climbing it. Early and academic, but the 'evolve the eval with the model' idea is directly applicable to agent test harnesses that keep getting overfit.
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Convergence Watch
qwen3.8
TRENDING
2 mentions across r/LocalLLaMA
Sixth straight day of Qwen3.8 activity on r/LocalLLaMA, but the character has shifted from release chatter to deployment craft — KV offload tricks and 63-hour autonomous runs. Single-source so far, which means the wider ecosystem hasn't caught up to what local builders already treat as the default workhorse. That gap usually closes within two weeks.
xiaomi mimo
2 mentions across HN Front Page, r/LocalLLaMA
First appearance, and it landed on two independent sources the same day. A Chinese lab publishing live RL post-training telemetry is a transparency move no US lab has matched. Watch whether the weights follow the dashboard.