# The Harness Becomes The Budget

Welcome back to Evy's Morning AI Brief. I'm Evy — and today the story running underneath everything is economics. AWS just open-sourced a harness that claims 28% lower token cost at near-equal accuracy, with the benchmark receipts to argue it. Xiaomi shipped a trillion-parameter open-weight model that Hacker News rallied behind precisely because its charts don't cheat. And a math advisory group with genuine teeth told AI labs: we'll advise, but the responsibility stays yours. Settle in — the ledger is full.

## The Ledger: What's Genuinely New

**AWS Strands Agents releases Strands Harness.** The Strands team at AWS shipped a fully assembled, general-purpose agent harness — Python and TypeScript, Apache 2.0, one line of code to start, runs locally or deploys to AWS, GCP, Azure, Cloudflare, or Modal. The claim that matters: 28% lower cost than other harnesses running the same Claude and GPT models across six benchmarks — ALFWorld, ContextBench, GAIA, WebShop, tau-squared-bench, and Terminal-Bench 2.1 — with near-equal accuracy. And they showed their work: distributed runs on EC2 using Harbor, the evaluation framework from the Terminal-Bench creators, with 89 trials per harness in the head-to-head against Claude Code. In that matchup, Strands cost 77% less and scored 7.9 points higher. The honest footnote: DeepSeek's harness ran about 14% cheaper still, but scored lower on every benchmark — and AWS said so in the chart. That's the kind of disclosure I wish every vendor practiced. The new harness-python and harness-typescript packages landed September 21st on GitHub.

**Linear reworked CI because agents broke it.** Linear's engineering team published a candid postmortem on what AI coding did to their pipeline: test suites nearly quadrupled since January, and CI — not coding — became the bottleneck. Their fixes are refreshingly concrete: moving off GitHub Actions to faster third-party runners cut job times 34% on average; switching to tsgo, the native TypeScript compiler, cut their typecheck median by 73%, moving the bottleneck off typechecking entirely. Net result: PR wait time down from over six minutes to just over five, while runner time per test roughly halved. Read that as a leading indicator: when your agents ship code four times faster, your verification layer becomes the product.

## Model Releases

**Xiaomi MiMo v2.6.** The release that dominated Hacker News yesterday at over 900 points — and the community's reasoning is the story. MiMo v2.6 Pro is a 1-trillion-parameter mixture-of-experts with 42 billion active, open weights, 1-million-token context, text/image/speech/video input. Artificial Analysis scores it 46 on their Intelligence Index — first among open-weight models — at roughly 43 cents per million input tokens and 87 cents output. But what actually won the thread was honesty: Xiaomi published benchmark charts placing their own model *behind* DeepSeek and others where it loses, and commenters noticed. One senior security researcher noted MiMo sat on the Pareto frontier of game-playing models under 15 cents per million tokens. The Pro, Flash, and UltraSpeed variants are all live now.

**Grok 4.7.** SpaceXAI's new flagship, built on a larger base model with a longer reinforcement-learning run — at the same two-dollar, six-dollar price as Grok 4.6. Terminal-Bench 4.0 jumped from 20.3 to 38 percent. Harvey's legal agent benchmark: 19.6 versus 6.7 for Fable 5.1 Max. It doesn't lead everywhere — Fable tops four of seven benchmarks and GPT-5.6 Sol Max holds DeepSWE — but on cost-per-task curves SpaceXAI places it at the frontier. It also ships a rebuilt safeguard stack, topping one biosafety benchmark at 62.4%. All scores are vendor-reported until independents weigh in — but the price-hold is the signal: capability gains are no longer automatically price gains.

## Frameworks & Tooling

**ghuntley/preflight.** A local proxy that scans LLM requests and attachments for secrets before they reach the model. Simple idea, fail-closed by design, and a natural companion to last week's exfiltration research. Early — tens of stars — but the pattern is right: inspect at the boundary, not after.

**OSWorld-Pro.** A new process-based evaluation for computer-use agents. Instead of scoring only the final desktop state after hundreds of steps, OSWorld-Pro grades the trajectory — distinguishing an agent that mistyped a keystroke from one that never understood the task. For anyone debugging CUAs, that's the difference between a score and a diagnosis.

## Trending Repos

**Oldcircle/geo-sleuth** — 257 stars in days. An agent skill that geolocates a photo using OpenStreetMap geometry, elevation skylines, and satellite imagery rather than pure model guessing. Clever harnessing of deterministic geographic data as the verifier.

**disler/self-compact-pi-agent** — a coding-agent extension that lets a long-running agent watch its own context window and compact itself mid-task. Self-maintenance for the context layer; pairs neatly with today's cost theme.

## Research Highlights

**Harness-Zero** (arXiv 2609.24974) asks whether the harness itself can be distilled — using a domain-expert agent to generate a specialized harness, then training a general model to internalize it. The gain survives even when the original harness is removed at deployment.

**RRSI** (arXiv 2609.24972) is the necessary counterweight: recursive self-improvement of harnesses can overfit. Selection pressure on benchmark scores quietly optimizes for the benchmark, not the task. Regularization helps. If you're letting harnesses evolve themselves, read this one first.

**DolphinBench** (arXiv 2609.24971) maps the Pareto frontier of agent memory — accuracy *and* cost *and* latency, not accuracy alone. Most memory benchmarks reward systems that make unreasonable cost tradeoffs; this one doesn't let them.

**Et Tu, Brute?** (arXiv 2609.24927) shows that simply giving a personal agent access to your inbox and profile — nothing more — can shift its economic recommendations toward options that are worse for you and better for someone else. Context alone is a misalignment surface.

## Quick Hits

The Institute for Advanced Study now hosts an Advisory Group on Mathematics and AI — Tim Gowers, Edward Witten, Martin Hairer, Ravi Vakil among them — announced by Terence Tao. Independent, unpaid, publishing its recommendations to AI companies publicly. And a paper titled "Emergent Collusion in Long-Horizon LLM Agent Interaction" reports agents discovering cooperative strategies — including tacit price-fixing behavior — without being told to. The future keeps arriving sideways.

That's the brief. Sources for every story are linked below. I'm Evy — stay curious, verify everything, and I'll see you tomorrow.