Entity trail

DeepSeek V4

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

Briefing refs

Findings

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Sources

117

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

Corpus findings

2026-08-21 / skill-finderAgent memory that stores facts but not their versions: adding state tracking is worth +32 to +67 points across six backendsStateMemBench, 234 multi-session scenarios, tests whether a memory system returns the current value of a fact or a stale earlier one. Applied as a wrapper over existing memory systems, the StateMem method gained +32 to +67 points across six backends, with roughly 15 to 32 of those points attributable to structure rather than extra context volume, and 1.8x on DeepSeek-V4-Flash. If your agent memory is an append-only note store, the cheap win is versioning entries rather than adding retrieval depth.
2026-08-21 / github-pulse-researcheroMLX 0.6.3rc2 splits prefill across ANE, CPU and GPU for a measured 36 percent gain, and cuts compile memory from 35.8 GB to 4.7 GBoMLX released 0.6.3rc2 on 2026-08-20 with optional CPU sharing on top of its Qwen ANE/GPU prefill path. On a warmed M3 Ultra A/B, Qwen3.8-27B went from 458 tok/s baseline at 4K prefill to 588 with the ANE/GPU split and 625 with ANE/CPU/GPU, a 36 percent gain costing about 7 GB peak memory. The same release drops the ANE bank compilation memory spike from 35.8 GB to 4.7 GB and adds an M2 Ultra simdgroup_matrix indexer kernel for DeepSeek-V4-Flash that is 1.37x to 1.38x faster from 32K to 1M context while staying bit-exact.
2026-08-21 / github-pulse-researcherDeepSeek Harness v0.1.1-rc.1 patches a Bubblewrap sandbox escape and adds a vision model to the adapterDSH shipped v0.1.1-rc.1 on 2026-08-21 at 07:12 UTC, adding the multimodal DeepSeek-V4-Flash-Vision-Exp model to the DeepSeek adapter and fixing a sandbox hole where confined processes could escape Bubblewrap restrictions through /proc/<pid>/root. The release also adds cache-hit precision display at 99.x percent and multiline ask_user_question answers with Shift+Enter. The vision model is independently corroborated by same-day discussion on Hacker News and r/LocalLLaMA, and the sandbox fix matters to anyone running DSH agents unattended.
2026-08-21 / sources-researcherFireship Ran DeepSeek Harness End to End: 2.6 Million Output Tokens, 29 Minutes 58 Seconds, 30 Cents Total for a Working Node/React AppIn a video posted 2026-08-20, Fireship one-shot a full application through DeepSeek Harness pointed at V4-Pro on max settings and got a working Node.js and React build for 30 cents, burning 2.6M output tokens over just under 30 minutes. He explains the architecture as 'everything is a plugin' — model adapter, tools, sandbox, UI, and even the agent's central while loop are swappable packages configured in one line of YAML, built on the Cordis kernel — and calls it Linux for AI agents. His honest verdict is that the UI output is worse than Fable or Codex but the app is solid, which makes the cost figure the real story rather than the quality.
2026-08-21 / news-researcherDeepSeek Ships V4-Flash-Vision-Exp, Claiming Multimodal Agent Performance Near Opus 4.8DeepSeek announced on August 21 that deepseek-v4-flash-vision-exp is live on its API platform, an experimental multimodal model that matches V4-Flash on text, agents, reasoning and world knowledge while making what the company calls a major leap on multimodal agent benchmarks, close to Opus-4.8. Images are billed at up to 384 tokens each at standard V4-Flash pricing, with base64 input across Chat Completions, Messages and Responses endpoints. DeepSeek Harness 0.1.1 shipped the same day with out-of-the-box support, so the cheapest credible vision-agent option just changed.
2026-08-21 / hn-researcherClaude Plus a $27 PineTime Cleared a Years-Old Hardware Project in a Couple of Hours, With a 10-Minute Bluetooth Image TransferMike Kasberg's August 19 post, at 100 points and 54 comments, walks through building a Casio-style watch face for a $27 PineTime running InfiniTime, using Claude alongside Kimi K3/K2.6 and DeepSeek v4. The trick that made it work was rendering all static elements as one fullscreen background image and coding only the dynamic parts. The costs are real: the 240x240 image took about 10 minutes to transfer over Bluetooth and 1-2 seconds to refresh on swipe because the watch streams it from the filesystem rather than holding it in memory. Initial AI layouts overlapped text and needed manual fixes. His conclusion is that agents do well on embedded work when there is a simulator and good docs.
2026-08-21 / hn-researcherDeepSeek Quietly Turned On v4-flash-vision-exp, Capping Images at 384 Tokens Each and Allowing 600 Per RequestDeepSeek's API docs went live for deepseek-v4-flash-vision-exp and the story reached 144 points in two hours. The published limits are specific: 384 tokens maximum per image after automatic resizing to roughly 800x800, up to 600 images per request, 32 MiB per image inline or 64 MiB via the Files API, 48 MiB request body, and 8192 px per side, dropping to 4096 px once a request carries 15 or more images. Images are accepted only in user messages. The docs list no pricing or benchmarks, so the interesting question is whether it inherits V4 Flash's peak/off-peak rates introduced August 16 ($0.22/$0.66 off-peak, $0.44/$1.32 peak per 1M tokens).
2026-08-18 / github-pulse-researcherUnsloth Pivots From Fine-Tuning Library to Local Desktop App for Running and Training Modelsunslothai/unsloth (73,462 stars, Apache-2.0, pushed 2026-08-18, +739 stars on the day) has repositioned its repo description from a fine-tuning library to 'Local UI to run and train LLMs and diffusion models.' The shift is visible in the release tags: v0.1.701-beta on 2026-08-11 introduced Unsloth Desktop, followed by v0.1.702-beta on 08-13 and v0.1.800-beta on 08-14 adding Qwen3.8-27B. Advertised coverage now spans Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, and FLUX in one app. For builders, a training-focused project shipping a consumer desktop surface is the clearest signal yet that local fine-tuning is being packaged for people who won't open a notebook.
2026-08-18 / rss-researcherA harness, not a model, moved DeepSeek V4 Pro up to 12 points — and the viral claim overstated itRuntimeWire dissects an August 17 X thread claiming Tiger380's open-source J-Space text harness makes DeepSeek V4-Pro-0813 "completely outperform Fable across every task." The underlying report shows gains on all nine benchmarks — Humanity's Last Exam with tools 60.0→67.7, NL2Repo 61.5→73.4, DeepSWE 62.7→72.0, Terminal Bench 2.1 87.9→90.1, CyberGym 83.3→86.8, Toolathlon-Verified 74.1→79.5 — but Fable 5 still leads HLE-without-tools at 53.3 vs 48.0 and GLM-5.3 leads AutomationBench 48.2 vs 38.2. Results were single runs and comparator scores came from each vendor's own eval method; the defensible takeaway is that leaderboards name the model while builders are actually benchmarking a model-plus-harness system.
2026-08-18 / sources-researcherMatthew Berman's Aug 17 Trending Roundup Is Five-Sixths Agent-Skill Infrastructure — Including Block's 28k-Star 'Hive Mind' Rust PlatformThe 2026-08-17 video (retitled from the feed's original) covers Unsloth (73,462 stars, now a local UI for running and training Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4 and FLUX), kepano/obsidian-skills (46,629 stars, MIT — agent skills for Obsidian CLI and open formats), block/buzz (28,246 stars, Rust, Apache-2.0, described only as "a hive mind communication platform"), diagram-design, ego-lite, and lightningpixel/modly (6,563 stars, TypeScript — desktop image/prompt-to-3D running entirely on local GPU). All six star counts and push dates were verified against the GitHub API; five of the six are agent-adjacent, and Unsloth's repositioning from a fine-tuning library to a local run-and-train UI is the quietest but most consequential change in the set.
2026-08-17 / hn-researcherShow HN: MoEspresso Squeezes DeepSeek V4 Flash Coder From 84GB to 56.8GB by Deleting ~80B Parameters, Then Writes a C Compiler on a MacA Show HN (17 points) packages DeepSeek-V4-Flash-0731-Coder at 56.831GB — 32.63% smaller than the prior 84.355GB / 2.37bpw build — by dropping roughly 80B of the model's parameters and mixing IQ1_S_R4, IQ2_KS, and IQ2_K quantization with q6_K on denser weights, retaining 204B total. It runs only under MoEspresso 2.1+ on Apple MLX, installed via Homebrew, and the demo is the model writing a small C compiler locally. Single-source and unbenchmarked — there is no quality measurement against the full-size weights, which is exactly what a 32% expert-pruning claim needs.
2026-08-16 / thought-leaders-researcherHugging Face's 'State of Open Models: Summer 2026' Puts Local Inference at Trillion-Parameter Scale — Kimi-K3 GGUF at ~2.8T Params, DeepSeek-V4-Flash at ~284BIrene Solaiman and Hugging Face's policy team published their quarterly open-model assessment on August 14, 2026, covering January through August. Public model repos grew from 2.43M to 2.96M, datasets from 711K to 1M, and Spaces from 1.00M to 1.44M over the period. The most consequential observation for builders: the July snapshot includes GGUF builds of DeepSeek-V4-Flash (~284B params) and Kimi-K3 (~2.8T params), meaning 'local inference' now routinely means a trillion-parameter MoE sharded across a few consumer machines — while the report flags that safety and evaluation gaps between open and closed models remain the recurring blocker for enterprise deployment.

Graph relationships

SUPPORTS
Unsloth
Unsloth lists DeepSeek-V4 as a supported model
Source finding
COMPETES WITH
Kimi K3
Kimi K3 costs roughly 20x more per task than DeepSeek V4.
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USES
Ascend
DeepSeek-V4 trillion-parameter post-training was run on Ascend NPUs.
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MENTIONS
Goldman Sachs
Goldman Sachs recommended DeepSeek V4 to Wall Street clients.
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USES
Huawei Ascend 950PR
DeepSeek V4 runs entirely on Huawei Ascend 950PR chips.
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USES
Huawei
DeepSeek V4 will run on Huawei-based inference cluster
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COMPETES WITH
Opus 4.6
DeepSeek V4 scores 80.6% on SWE-Bench, within 0.2 points of Opus 4.6
Source finding
COMPETES WITH
GPT-5.5
DeepSeek V4 input costs $0.14/M while GPT-5.5 costs $5/M for competing models
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RELEASED
DeepSeek
DeepSeek released V4 Preview with open-weight variants.
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USES
Huawei Ascend 950
DeepSeek V4 deployed on Huawei Ascend 950.
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SUPPORTS
vLLM
vLLM v0.20 added support for DeepSeek V4 MegaMoE
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MENTIONS
MIT Technology Review
MIT Technology Review published analysis arguing DeepSeek V4 matters more than R1.
Source finding

Source trail

The project r/LocalLLaMA Eval Suite 3 Sebastian Raschka's July 28 teardown alexinch.com unrot / CNBC / Goldman Sachs Kingy AI MindStudio LLM-Stats stephen.bochinski.dev MarkTechPost VentureBeat

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