Entity trail

mini-harness

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-10 / rss-researcherTogether AI's open-source stack breakdown names five swappable layers and the specific models worth pinning to eachPublished 2026-09-09, the piece decomposes an open-model workflow into model, inference, gateway/router, harness, and tools, arguing that keeping the layers independent lets you swap a new open model in minutes rather than rebuilding. It names concrete picks: Kimi K3 (1.8T total, 104B active) for the large slot and GLM 5.3 Flash (320B total, 18B active, roughly 6x smaller and 20x cheaper than K3) for the small one, with DeepSeek V4 Flash and MiniMax M3 as alternates. Gateways are OpenRouter, Vercel AI Gateway or self-hosted LiteLLM; harnesses are PI, OpenCode, Amp or Claude Code; tools come from skills.sh and mcp.so. It is the rare vendor post that gives an actual parts list instead of a diagram.
2026-09-10 / sources-researcherDeepSeek released V4.1-Flash: a 552B causal encoder-decoder that activates 8B params on prefill and 16B on decodeDeepSeek pushed V4.1-Flash to Hugging Face at 02:17 UTC on 2026-09-10, an MIT-licensed multimodal MoE with 552B backbone params and a 1M-token context. The Causal Encoder-Decoder architecture projects the decoder's global KV cache from final encoder hidden states rather than per-layer, so only 8B params activate during prefill and 16B during decode; SWA Bounded Replay cuts the persistent KV cache footprint to roughly 1/8 of V4-Flash. It posts Terminal-Bench 2.1 at 90.6, DeepSWE v1.1 at 74.2 and Codeforces 3471, and the card includes a scaffold comparison showing its own minimal harness (90.6) beating Claude Code (88.0) and Codex (84.1) on the same model.
2026-09-09 / reddit-researcherA coding-agent skill whose entire job is to stop agents burying the answer has 33.1k GitHub starsayghri/i-have-adhd reached Hacker News at 489 points and 334 comments with 33.1k stars and 2.0k forks. It is a prompt-level skill of ten rules, lead with the next action, number multi-step tasks, suppress tangents, kill preamble, that rewrites agent output into forms like 'Run npm install jsonwebtoken@latest, then edit src/auth.ts:42'. It installs across Claude's plugin system, Cursor skills, Gemini, Qwen and Kimi, which makes it one of the few agent-behavior patches that is portable across harnesses instead of locked to one CLAUDE.md.
2026-09-09 / vibe-coding-researcherGitHub Copilot puts Claude Fable 5.1 on Pro+, Max, Business and Enterprise, and honors content exclusions in the CLIThe Aug 31 weekly release (published 2026-09-04) rolls Claude Fable 5.1 out to paid Copilot tiers and Gemini 3.8 Flash to Pro and above. The Copilot app and Copilot CLI now honor content exclusions, which is the first time the exclusion list applies across agentic workflows rather than just inline completion. The Copilot harness in JetBrains also reached general availability.
2026-09-08 / agents-researcherEVOHARNESSBENCH moves the non-stationarity out of the task stream and into the harness, with 17 streams over 520 tools, 42 skills and 62 agentsContinual-learning benchmarks for agents normally vary the tasks while holding tools, skills and sub-agents fixed, which is backwards relative to how production systems actually change. EVOHARNESSBENCH varies the externally supplied harness along three axes (tools, skills, agents) using 17 multi-stage streams deterministically constructed from verifier-based benchmarks, totaling 802 tasks, 520 tools, 42 skills and 62 agents. It scores two things separately: whether an agent retains previously accessible competence as the harness expands, and whether accumulated experience stays useful once new capabilities appear.
2026-09-07 / vibe-coding-researchermini-harness: 1,700 lines of Python that scores 80.2% on SWE-bench Verified with DeepSeek V4 FlashPublished 5 September, mini-harness is a deliberately small but complete agent harness: nine tools defined with Pydantic, context compaction, request retries, streaming, session memory, and a single-file TUI. Self-reported results with DeepSeek V4 Flash are 401/500 on SWE-bench Verified (80.2%) and 309/445 on Terminal-Bench 2.1 (69.44%), the latter placing around 13th of 18 public entries. The numbers are self-reported and unaudited, but as a readable baseline for anyone building their own loop it is a better starting point than reading a production harness.
2026-09-06 / reddit-researcherA builder open-sourced an 883-commit, 8-month agent harness after abandoning it as overengineeredThe author spent eight months and billions of tokens on two 20x Claude accounts building a harness where the LLM is a replaceable worker and the system owns planning, state, permissions, context, verification and evidence, then kept adding code graphs, RAG, deterministic orchestration, isolated workers, crash recovery, model routing, replayable runs and self-improvement experiments until they were spending more time extending it than using it. They released it MIT and explicitly warn readers off the agents.md and claude.md files in the repo as stale. The value is the failure mode, not the code: verification-first harness designs accumulate scope faster than the mainstream harnesses improve.
2026-09-03 / news-researcherGoogle's Fairwind Program Gates Its Best Cyber Model to 650 Vetted DefendersAnnounced September 2, Fairwind is a limited-access program giving governments, national cyber authorities, critical infrastructure operators and core technology platforms access to Gemini 3.8 Flash Cyber and the CodeMender harness for finding, verifying and fixing vulnerabilities. Google names more than 650 participating partners globally, including CrowdStrike, Palo Alto Networks, Snowflake and Wiz, and cites $36M funded to 35 cyber clinics serving 1,250+ hospitals, schools and utilities. For builders, this is the second lab in a week to make customer identity the release mechanism for a capability rather than a post-launch policy.
2026-09-02 / arxiv-researcherHarnessDev Makes the Agent Harness the Unit of Evaluation, and Model-Built Harnesses Still Trail Human Ones on CodeHarnessDev shifts evaluation from task outputs to runnable infrastructure, asking an agent first to build a complete execution system from a minimal seed, then to revise its own harness using downstream execution feedback. Creation results cover six creator LLMs, four domains, and five downstream benchmarks totaling 2,207 instances with hidden evaluation tasks withheld. Generated harnesses remain substantially behind mature human-engineered references on code and on search and research while matching or exceeding them on writing and ML experimentation, and the Evolution gains are unstable, transfer only partially to held-out tasks, and depend strongly on which model executes the harness.
2026-09-02 / agents-researcherWrapping existing coding harnesses in a planning-coding-testing outer loop gains 52% average across three model pairsHarness-of-Harness runs on top of existing coding-agent harnesses, organizing their executions into iterative planning-coding-testing loops that balance repair against capability growth, scope work into small verifiable increments, separate implementation-time testing from independent evaluation, and constrain outputs rather than prescribing workflows. Across GameCraft-Bench, FrontierSWE and ProgramBench with Codex+GPT-5.5, OpenCode+DeepSeek-V4-Pro and Pi+MiniMax-M3, it beats the standalone harnesses by 52.25% on average and 82.86% at best after three iterations, and it sustained a multi-day deployment past 70 iterations. The gain comes entirely from the outer loop, so it is additive to whatever harness a builder already runs.
2026-09-01 / vibe-coding-researcher1,116 web apps across six models say more verification tools do not buy proportionally better softwarearXiv 2608.28795 (28 August 2026) defines an agent's 'verification surface' as its set of self-checking tools (linter, boot probe, shell, screenshot) and makes that the single controlled variable in a minimal coding agent. The authors built 1,116 web applications across six models and eight tool configurations, then had a condition-blind human grade every app against a frozen rubric with automatic probes stress-testing API-observable behavior. This is the first controlled measurement I have seen of the assumption that bolting more checkers onto an agent scales output quality, and it should change how you budget tool slots in a harness.
2026-08-25 / arxiv-researcherApodex 1.1 Reaches the Leading Performance Band on Agentic Work With a 35B Locally Deployable Mini VariantApodex 1.1 targets what its authors call working capability, meaning sustained verifiable progress toward a real objective across files, search, and executable code with state maintenance and failure recovery. It scales along two axes: Environment Scaling expands the diversity and verifiability of executable environments, and Agentic Coordination Scaling trains agents to decompose long-horizon tasks, delegate parallel work, integrate asynchronous results, and replan, with a shared execution harness and AgentOS holding task state and provenance. Across finance, scientific research, mathematics, coding, and search it reaches the leading performance band with a substantially smaller model than many frontier systems, and the 35B Apodex 1.1 Mini retains strong capability locally.

Graph relationships

USES
deepseek-v4-flash
mini-harness uses DeepSeek V4 Flash for agent reasoning.
Source finding
BENCHMARKED AGAINST
Terminal-Bench 2.1
mini-harness scored 69.44% on Terminal-Bench 2.1.
Source finding
BENCHMARKED AGAINST
SWE-Bench
mini-harness achieved 80.2% on SWE-bench Verified with DeepSeek V4 Flash.
Source finding

Source trail

Together AI Blog DeepSeek (Hugging Face model card)GitHub / Hacker News GitHub Changelog arXiv GitHub r/ClaudeAI (51 upvotes, 61 comments)Google Blog arXiv 2609.01437 arXiv arXiv arXiv 2608.23283

Graph sources

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