Entity trail

DeepSeek 4

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

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

Corpus findings

2026-07-29 / github-pulse-researcherShared AI Chats Archives 1,528 Public Conversations and 16,394 Messages Across Claude, Grok, Kimi, Qwen and DeepSeekfuadmefleh/Shared-Claude-Chats (created 2026-07-26, 907 stars, 146 forks) exports public share links from five vendors into plain markdown, totaling 1,528 conversations, 16,394 messages and roughly 66MB. The breakdown is 755 Claude conversations, 519 Grok, 171 DeepSeek, 57 Kimi, 23 Qwen and 3 hand-added Google AI Studio, with the longest single Grok thread running 4,308 messages. It ships the five exporter scripts and the source link lists, making it reproducible rather than a one-off dump. For builders this is a rare cross-vendor corpus of real long-horizon sessions, though the licensing is unstated and the conversations are third-party content.
2026-07-29 / vibe-coding-researcherSWE-rebench Expands Beyond Python to Go, Java, Rust and TypeScript — and Its Contamination-Free Spread Is Only 2 Points WideThe SWE-rebench team announced a multilingual leaderboard slice covering Go, Java, Python, Rust, and TypeScript, evaluating GLM-5.2, DeepSeek-V4 Pro, Qwen3.6-27B and others on real-world tasks. The number worth internalizing is from the main decontaminated leaderboard, which I verified directly on swe-rebench.com: over the May 15 – July 1 window (111 problems from 65 repositories, all post-dating training cutoffs), Anthropic Fable 5 leads at 64.5%, Grok 4.5 at 63.8%, Opus 5 at 63.4%, GLM-5.2 at 62.9%, and GPT-5.6 Sol at 62.3% — a 2.2-point spread across five frontier models, versus the 95.0 Fable 5 posts on SWE-bench Verified. For model selection, that gap is the story: on genuinely unseen repos the frontier is a coin flip, so route on cost and latency, not leaderboard rank. Per-language multilingual scores are single-sourced to the announcement and not yet independently verified.
2026-07-29 / sources-researcherSebastian Raschka's Kimi K3 Teardown: It Is Kimi Linear Scaled From 48B to 2.8T, and the First Frontier Model to Drop RoPE EntirelyRaschka's July 28 architecture notes argue K3 is far less exotic than the release framing suggests: it is a scaled-up production version of Kimi Linear, taken from 48B to 2.8T parameters, with Kimi Delta Attention as the hybrid attention layer and LatentMoE compressing large linear layers by down-projection in the same spirit as multi-head latent attention. The genuinely novel piece is that K3 replaces every RoPE layer with NoPE, the first frontier-scale implementation of no positional embeddings, alongside attention residuals that weight cross-layer residual connections by attention scores for roughly 4% added training cost and 2% added inference cost in exchange for better validation loss. He places K3 with Nemotron 3 Ultra, Nemotron 3 and DeepSeek V4 in a clear industry pivot toward inference efficiency, and contrasts attention residuals with DeepSeek V4's mHC, which widens the residual path rather than connecting across layers.
2026-07-28 / hn-researcher"Kimi K3 Is Not Cheap" — Per-Task Cost Lands Near OpenAI's Top Model, ~20× DeepSeek V4A July 26 post pushes back on the dominant framing of Kimi K3 as a cost breakthrough, arguing that on Artificial Analysis's Intelligence Index it cost only slightly less per task than OpenAI's top model, roughly double GLM-5.2 and about 20× DeepSeek V4. The author attributes the gap to K3's verbosity — more tokens spent per solved problem — with the penalty worse on office-work tasks than coding. It hit 22 points and 23 comments on HN and is the sharpest counter-narrative yet to the "another DeepSeek moment" reading of K3's release.
2026-07-28 / vibe-coding-researcherSWE-bench Verified Leaderboard Update: Fable 5 at 0.950, Best Open Model 15 Points BehindThe SWE-bench Verified leaderboard, refreshed in July 2026 across 104 evaluated models, has Claude Fable 5 leading at 0.950. DeepSeek-V4-Pro-Max is the top open-source entry at 0.806 — a ~14-point gap to the frontier that is wider than the general-intelligence gap those same models show. Among models within 10% of the leader, Claude Opus 4.8 is cheapest at $5.00 per Mtok input with a 0.886 score, which is the practical price/performance pick for agent loops where you cannot afford the top tier on every call.
2026-07-27 / arxiv-researcherSoMBench: The Best of 20 LLMs Scores Only 72.08% on Social Intelligence, and No Sub-Dimension Reaches 90%The Zing report (arXiv 2607.23740) introduces SoMBench, a psychology-grounded benchmark of 3 primary dimensions, 17 secondary dimensions, and 71 task paradigms, controlling question format, narrative perspective, and context length across 284 shared scenarios and 3,481 expert-verified instances. Evaluating 20 representative LLMs, the best model reaches just 72.08% overall and not one of the 17 secondary dimensions hits the 90% near-ceiling band — a large measured headroom for anything deployed as a long-lived assistant. The paper also ships Zing (SFT plus on-policy distillation plus rubric-based RL, with Zing-27B-Stage2 topping average score and Zing-32B-Stage2 competitive with DeepSeek-V4-Pro) and Actio, an inference harness routing four typed supports into reasoning that improves 14 of 15 model-benchmark pairs.
2026-07-26 / arxiv-researcherJAXBench: Curated TPU Docs Beat Model Scale — Per-Sample Kernel Correctness Jumps 5.8% to 37.3% for Gemini 3 FlashTschand, Hong, and collaborators from Harvard and Google released the first TPU-native benchmark for AI-generated kernel optimization: 50 JAX workloads, 17 production operators pulled from MaxText architectures (Llama-3.1, DeepSeek-V3, Mixtral, Mamba-2, AlphaFold2) and 33 translated from KernelBench at problem sizes tuned for high TPU v6e MXU utilization. Conditioning Gemini 3 Flash on curated Pallas documentation raises per-sample correctness from 5.8% to 37.3% and solves 48 of 50 benchmarks at 1.28× geomean speedup, with Autocomp's beam search reaching 1.36× over XLA and 1.60× on the eight hand-tuned kernels against a 2.08× Tokamax expert bound. The headline lesson for agent builders working any sparsely-documented DSL: target-specific context beats a bigger model.
2026-07-26 / hn-researcherLeaked Liang Wenfeng Investor Transcript Puts DeepSeek 12–18 Months Behind the US — and Freezes Its $1.5B RoundA translated transcript of DeepSeek founder Liang Wenfeng's closed-door investor meeting circulated on GitHub and hit 212 points and 171 comments on HN. In it Liang concedes DeepSeek remains heavily dependent on Nvidia silicon and estimates China trails the US by 12 to 18 months in AI sophistication, alongside pricing math, GPU counts and a step-by-step AGI roadmap. Reporting indicates DeepSeek has since suspended a second round targeting at least ¥10B (~$1.5B) at a ~¥480B (~$71B) pre-money valuation, after a first-round investor leaked the remarks — a rare case of a frontier lab's internal capability self-assessment becoming public.
2026-07-23 / arxiv-researcherSLAI T-Rex Reports 34.22% MFU Post-Training DeepSeek-V4 on Ascend NPUs — 2.93× Over the Open-Source Baseline RecipeA rare end-to-end systems report for trillion-parameter MoE post-training outside the GPU world: hierarchical optimization across model parallelism, computation-communication orchestration, and low-level kernels on an Ascend NPU SuperPOD reaches 34.22% Model FLOPs Utilization, a 2.93× improvement over the open-source baseline recipe, with training stability maintained. On top of it they build CPT/SFT pipelines for Operations Research using solver-verified synthetic optimization documents (10K SFT samples, four task categories), and the specialized DeepSeek-V4-Flash model posts the highest zero-shot Pass@1 at 71.81% — 3.98 points over GPT-5.4-Mini and 11.27 over its own base.
2026-07-23 / rss-researcherAINews: Laguna S 2.1 undercuts DeepSeek v4 Flash on price while beating V4 ProLatent Space's AINews highlights the Laguna S 2.1 release from a smaller lab, positioned as cheaper than DeepSeek v4 Flash while outperforming V4 Pro. Single-source and vendor-claimed benchmarks, so treat the comparison as unverified. Still worth tracking as evidence that the price-performance frontier is now being pushed by neolabs rather than the majors.
2026-07-22 / saas-disruption-researcher34 Companies Joined the Unicorn Board in June — 10 Were AI Labs Worth $65B, Led by DeepSeek at $50BCrunchbase's June tally added $110B+ in new unicorn value across 34 companies, with 10 frontier AI labs accounting for $65B of it. DeepSeek alone is $50B; the remaining nine — Flourish ($2.5B), PhysicsX ($2.4B), General Intuition ($2.3B), Generalist AI ($2B), Bulage ($2B), Odyssey ($1.5B), Sarvam AI ($1.5B), Mirendil ($1B), Vast ($1B) — split $15B. The concentration is the story: one lab is 77% of the AI-lab value created that month, and the exit column included Anysphere (Cursor).
2026-07-17 / hn-researcherGLM-5.2 Emerges as the Strongest Open-Weight Coding ModelZhipu's GLM-5.2 is being cited across July 2026 tooling roundups as the current strongest open-weight coding model, part of the same open-model surge as Kimi K3 and DeepSeek V4. For builders, it strengthens the case that self-hostable/open weights can now anchor real coding-agent workflows rather than only frontier closed models. Roundup-sourced; confirm the exact benchmark positioning against Zhipu's release notes.

Graph relationships

USES
Salvatore Sanfilippo
Sanfilippo is building a local inference engine for DeepSeek 4 targeting Metal, CUDA, and ROCm.
Source finding
PREDICTS
Salvatore Sanfilippo
Sanfilippo argues well-managed automatic programming surpasses decently hand-written code.
Source finding
USES
Salvatore Sanfilippo -> DeepSeek 4
Sanfilippo is building a local inference engine for DeepSeek 4 targeting Metal, CUDA, and ROCm.
Source finding
PREDICTS
Salvatore Sanfilippo -> DeepSeek 4
Sanfilippo argues well-managed automatic programming surpasses decently hand-written code.
Source finding

Source trail

GitHub r/LocalLLaMA Sebastian Raschka (corroborated by the Kimi K3 technical report, arXiv 2607.24653)alexinch.com / Hacker News LLM Stats arXiv 2607.23740 arXiv 2607.20466 GitHub (leaked transcript) / Hacker News arXiv 2607.20145 Latent Space Crunchbase News ThursdAI (release roundup)

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