Entity trail

Kimi

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

Briefing refs

Findings

Edges

Sources

107

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

Corpus findings

2026-09-06 / thought-leaders-researcherArmin Ronacher asks whether LLMs are quietly steering independent builders onto identical projectsIn "Latent Powers" (September 5), Ronacher describes reflashing a Carlinkit Mini Ultra CarPlay dongle after Fable, Sol and Kimi K3 all pointed him at CatPlay, a Rust reimplementation of the CarPlay protocol, then discovered an acquaintance had independently arrived at nearly the same project through similar model conversations. He cites Lucas Meijer's observation that generating HTML reports rather than Markdown became a default as models got better at it. His question for anyone building with agents: "How much of what we build comes from eliciting the same latent capabilities from the same models?"
2026-09-06 / vibe-coding-researcherFrontierHarness Eval: same model, nine harnesses, 17.5x cost spread and a 16-point pass-rate spreadThe frontier-harness-eval/eval repo (144 stars, created 2026-08-31, pushed 2026-09-06) ran Kimi K3 through nine coding-agent harnesses in 12 configurations over the same 30 software-engineering tasks, 360 runs total. Codex led on pass rate at 66.7% ($3.47 median cost per pass), Claude Code matched DSH Creator at 63.3% but cost $18.34 per pass with only 67.8% median cache hit, and Exo Harness passed 53.3% at $1.05. The harness, not the model, is the variable nobody controls for, and cache behaviour is where the 17.5x cost gap lives.
2026-09-05 / arxiv-researcherFour Frontier Models Score Above 0.81 BERT-F1 Extracting Architectural Decisions From Commits and Still Miss the RationalearXiv 2609.03721 tests whether LLMs can recover architectural design decisions hidden inside source code commits, using Gemini 3 Pro, DeepSeek R1, Kimi K2 and Qwen3 with zero-shot and few-shot prompting on 30 developer-written ADDs from open-source projects, scored with ROUGE-L, BLEU, METEOR and BERTScore plus manual review of the Gemini outputs. All models reached BERT-F1 above 0.81 and few-shot improved alignment (Gemini 0.828 to 0.847), but manual review found the generated ADDs were often too long, implementation-focused, and missing the rationale behind the decision. The gap between a strong similarity score and a useless output is the finding, and it is a caution for anyone trusting text-overlap metrics on documentation-generation tasks.
2026-09-04 / arxiv-researcherVestigeKV Evicts a NoPE KV Cache Using a Vestigial RoPE Branch, Holding 1.00 Retrieval at 32x CompressionAttention-observed selectors like H2O and SnapKV collapse to 0.00-0.33 needle retrieval on a NoPE MLA model because a long-lived cache must be compressed before the queries that will read it exist. On Kimi Linear, VestigeKV evicts by a query-independent signal the cache already carries, the 64-dimensional decoupled branch that NoPE training repurposes from a RoPE vestige into a salience channel, reading 11% of each row and moving non-top rows exactly into a GPU-resident archive rather than deleting them, with no training, quantization or kernel change. Retrieval holds at 1.00 under 8x and 0.92 under 32x from 8k to 65k context, and the effect is NoPE-exclusive: the identical operator on a RoPE MLA collapses to 0.08.
2026-09-04 / rss-researcherGitHub is retiring four Copilot models on October 2, 2026GitHub published a changelog entry on 2026-09-03 announcing the upcoming deprecation of selected Copilot models, filed under its Retired/Deprecations category. Secondary coverage lists the four as Gemini 3.5 Flash, Gemini 3.6 Flash, Kimi K2.7 Code and Claude Opus 4.7, retiring October 2, 2026, with Gemini 3.8 Flash, Kimi K3 and Claude Opus 5 as the pushed replacements. The GitHub changelog confirms the entry and its date, but the specific model list comes from one secondary source, so check the entry itself before repinning anything.
2026-09-04 / github-pulse-researcherLobeHub v2.2.16 lands 246 merged PRs, adds Factory Droid over ACP and an Elasticsearch search pathThe 2026-09-04 release covers 246 merged PRs from 9 contributors since v2.2.15. Factory Droid joins over ACP, TRAE and Grok Build gain provider binding, and the deployment-provided LobeHub connection expands across Claude Code, Codex, Kimi Code, Pi and Grok Build (#18737, #18775, #18785, #18811, #18932). Full-text search gains a provider-neutral layer with a durable outbox, resumable indexing and an opt-in Elasticsearch Docker Compose profile that leaves the default self-hosted setup unchanged (#18807, #18902, #19028), and the lh CLI adds workspace selection, resilient Goal execution, operation inspection and replay, and execution snapshots (#18797, #18874, #19000, #19015, #19020).
2026-09-03 / saas-disruption-researcherFrontierHarness Eval: Same Model, Nine Harnesses, 17.5x Spread in Cost Per TaskRunta published FrontierHarness (Show HN, 2026-09-02, 75 points), benchmarking nine agent harnesses (Codex, Claude Code, OpenCode, Pi, Oh My Pi, DeepSeek Harness, Kimi Code, Exo Harness, Hermes) on software engineering and terminal tasks with an identical cold start and fresh checkpoint restore on every run to kill warm-cache bias. Cost per task ranged from $1.05 to $18.34, about 17.5x, while cost per passing task ranged $0.0615 (OpenCode) to $0.2880 (Claude Code), about 4.7x. Codex led on quality at 66.7% pass for $3.47 per task; Exo Harness was cheapest at $1.05 with 53.3% success. The harness, not the model, is now the dominant cost variable in an agentic coding budget.
2026-09-02 / hn-researcherBaseten Splits LLM Inference Techniques Into Two Classes: Ones That Move You Along the Latency-Throughput Frontier and Ones That Push It OutPhilip Kiely's September 1, 2026 piece argues that batch sizing, parallelism strategy (tensor, expert, and attention data parallelism) and quantization only trade latency against throughput, while kernel and runtime optimization, speculative decoding, and prefill/decode disaggregation move the whole frontier. The framing matters because frontier-pushing techniques compound multiplicatively, so two 2x improvements give you 4x of budget to spend on whichever axis you care about. The analysis assumes serving models like GLM-5.3 or Kimi K3 for agentic coding with KV cache reuse and KV-aware routing enabled.
2026-09-01 / arxiv-researcherWebWorld Uses the Browser as the Judge a VLM Cannot Fool, Beating Its Own Base Model by 14.9 PointsSelf-improving web code normally has the same VLM proposing and judging the repair, so visual plausibility substitutes for whether the page works; WebWorld makes the browser the counterparty instead, compiling each VLM critique into a typed interaction contract and re-executing the candidate. An acceptance certificate issues only when target progress holds and every previously verified capability is preserved, and only certified transitions reach the SFT export, forming a quality ratchet. WebWorld-27B improves its raw base by 5.3 points on HTMLBench-400 and 14.9 points on MiniAppBench-Val, reaching Kimi-K2.6 and GPT-5.4 levels on interactive HTML generation, and equal-size ablations show the lift nearly vanishes without the certificate.
2026-09-01 / arxiv-researcherData-Science Code Efficiency Does Not Track Correctness: Kimi-K2.5 Ranks Last on Pass, First on SpeedDSEffi-Bench is the first benchmark targeting execution efficiency in LLM-generated data science code, with 1,000 instances across 10-plus libraries, stress-testing harnesses and human-validated references, evaluated on 16 models. GPT-5.4 leads correctness at 66.9% Pass but its 71.7% efficiency score barely beats GPT-5.4-mini's 71.6% despite solving 47 more tasks, while Kimi-K2.5 is lowest in correctness among frontier models at 40.2% yet posts the best efficiency score at 73.6%. A human-annotated taxonomy finds 79.1% of efficiency deficits come from domain-specific causes rather than algorithmic complexity, and library-conditioned routing approaches Claude-Opus-4.6 Best@3 efficiency at 13.0x lower cost.
2026-09-01 / github-pulse-researcherLMDeploy 0.17.0 integrates DeepEPv2 and adds Kimi K2.6 plus a Mooncake store KV connectorPublished 2026-09-01, LMDeploy v0.17.0 integrates DeepEPv2 (#4783), adds PyTorch-engine support for Kimi K2.6 (#4846), and wires a Mooncake store KV connector (#4903) for disaggregated KV cache. Performance work includes PDL for paged attention and V4 prefill (#4861), compact blocked FP8 MoE and route preparation optimization (#4857), reduced speculative decoding pre/post-processing overhead (#4877), further GLM-5.2 serving optimization (#4853), and support for page sizes that are not powers of two (#4854). Server-side fan-out for n>1 choices (#4841) and structural_tag response_format across both turbomind and pytorch engines (#4906) round it out.
2026-09-01 / thought-leaders-researcherFireworks makes its Training API generally available with named customer deltas: Harvey 19.7% vs 10.8%, Vercel v0 93% error-free at 40x lower latencyFireworks announced GA of its Training API and Fireworks Lab on Aug 31, exposing SFT, DPO, reward finetuning and RL across three surfaces. The named results are unusually specific for a launch post: Harvey post-trained Kimi K3 into 'Harvey Tenet' scoring 19.7% all-pass on LAB against 10.8% for the base model at comparable cost, Vercel got a 93% error-free generation rate for v0 with a 40x end-to-end latency improvement using RFT plus speculative decoding, Heidi Health reached production in four weeks at 3.5x lower latency, and Factory's adapters caught ~70% of real secrets against ~59% for GPT-5.5 at a 5% false-alarm budget.

Graph relationships

RELEASED
K3
Kimi released K3 language model
Source finding
RELEASED
Kimi Attention Residuals
Kimi published arXiv:2603.15031 on learned per-layer scalar weights for residual connections
Source finding
RELEASED
Moonshot AI
Moonshot AI's Kimi team uses attention residuals in production.
Source finding
USES
oh-my-openagent
OMO supports Kimi alongside other language models.
Source finding
BUILT BY
Moonshot AI
Kimi is a chatbot developed by Moonshot AI.
Source finding
USES
claude_codex_bridge
claude_codex_bridge mixes Kimi with other agents
Source finding
RELEASED
Moonshot AI
Moonshot AI developed Kimi, a chatbot with strong commercial traction.
Source finding
SUPPORTS
Unsloth
Unsloth supports Kimi-K2.5 for local fine-tuning.
Source finding
USES
Kimi Delta Attention
Kimi uses hybrid-attention Kimi Delta Attention architecture.
Source finding
USES
Delta Attention
Kimi Delta Attention delivers up to 6x faster throughput at 1M-token context.
Source finding
RELEASED
Moonshot
Moonshot AI proposed Kimi with Kimi Delta Attention for long-context prefill.
Source finding
BUILT BY
Moonshot AI
Kimi is developed by Moonshot AI.
Source finding

Source trail

arXiv 2608.30248 Thinking Machines Lab GitHub GitHub r/LocalLLaMA Hugging Face GitHub The 0731 checkpoint arguing it's the killer app for DGX Spark v0.10.0 Binaryify/open-kimi-ppt-skill mpai

Graph sources

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