Source Hugging Face Blog
Measuring benchmark optimization in speech recognitionAug 21
states limits2.5k words
What we saw
This source gives a title and a link, nothing to quote.
They claim — We evaluated 11 widely used open-source ASR models and found that several of the highest-scoring systems reproduced benchmark transcripts from the VoxPopuli English and LibriSpeech (clean…
Published 2026-08-21
Fetched 2026-08-25 10:14 UTC · 8de96fd631a45d0c
Torn from — the page itself
infinite video editingstreaming video generatorSource HF Daily Papers
Prompt EngineeringContext EngineeringSource HF Daily Papers
omni-modal large language modelsOmniAssistBenchSource HF Daily Papers
OmniAssistBench: Assistant-style Interaction Benchmark for Omni-LLMs28 upvotes2 comments
omni-modal large language models9 authorsstates limits
What we saw
“OmniAssistBench evaluates real-time interactive video assistants by reverse-engineering multi-turn interaction videos, revealin…”
They claim — Results show that the proprietary Gemini-3-Pro reaches 66.4 out of the max point of 100, while the open-source Qwen3-Omni-Instruct achieves 51.2.
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · cb7314dbba846623
Torn from — the abstract
Thread ↗ omni-modal understandingPer-vGridSource HF Daily Papers
Source Hugging Face Blog
Up to 3.2x Faster Inference with LFM2.5-DSparkAug 20
no limits stated1.3k words
What we saw
This source gives a title and a link, nothing to quote.
They claim — Faster inference : up to 3.18 throughput improvement on a GPU and up to 2.87x on-device.
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · b41fa9c74f86007c
Torn from — the page itself
Pythonai-videoSource GitHub
cclank/lanshu-create-ai-presenter-video883 stars153 forks
MITPythonpushed 4d ago
Location, estimated · Not stated
What we saw
“Provider-neutral Codex Skill for producing verified AI presenter videos from a script and an authorized presenter image.”
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · f2ced8b6406f4329
What the account wrote — The account left it blank
Torn from — the README
Mixture-of-ExpertsMaximal Update ParameterizationSource HF Daily Papers
Source OpenAI
Introducing AI FuturesAug 20
What we saw
“Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and ind…”
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · ec2332dc6523ff28
Torn from — the listing only (the original would not open)
Source OpenAI
Stampli cuts launch hours by 68% using ChatGPT WorkAug 20
What we saw
“With a fixed deadline and design resources committed elsewhere, Stampli used Codex and ChatGPT Work to compress weeks of launch…”
Published 2026-08-20
Fetched 2026-08-25 10:14 UTC · 1476f459afd654a9
Torn from — the listing only (the original would not open)
SWE-bench Sciencescientific software engineeringSource HF Daily Papers
SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?61 upvotes4 comments
SWE-bench Science5 authorsstates limits
What we saw
“SWE-bench Science benchmarks coding agents on scientific software repair, revealing failure mechanisms and mixed effects of sci…”
They claim — Even the best-performing agent, Claude Code with Opus-5 (max), achieves a pass@1 below 50%, highlighting the substantial challenges posed by scientific software engineering.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · bbf6eac293bcc02d
Torn from — the abstract
Thread ↗ text-generationmlxSource Hugging Face
OBLITERATUS/Qwen3.8-27B-OBLITERATED713 likes390k downloads
apache-2.0built on Qwen3.8-27B
What we saw
This source gives a title and a link, nothing to quote.
They claim — A few subjects like philosophy and European history actually improved (+6pp and +4pp respectively), while abstract algebra and formal logic saw larger drops.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · 30b05f2788443301
Torn from — the model card
any-to-anytransformersSource Hugging Face
sensenova/SenseNova-U1.5-8B-MoT141 likes2.7k downloads
apache-2.0
What we saw
This source gives a title and a link, nothing to quote.
They claim — More efficient native 4K generation: more coherent global structure, color harmony, and stable high-resolution output with improved generation efficiency.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · dc5f49c564074f43
Torn from — the model card
text-to-speechtransformersSource Hugging Face
Audio8/Audio8-TTS-Preview-0.1b151 likes3.6k downloads
other
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · e0cf23a392b802ca
Torn from — the model card
text-generationtransformersSource Hugging Face
ornith-ai/Ornith-1.5-9B-GGUF191 likes1.1M downloads
mit
What we saw
This source gives a title and a link, nothing to quote.
They claim — 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.
Published 2026-08-19
Fetched 2026-08-25 10:14 UTC · 4195fce25151562f
Torn from — the model card
text-generationtransformersSource Hugging Face
ornith-ai/Ornith-1.5-9B206 likes98k downloads
mit
What we saw
This source gives a title and a link, nothing to quote.
They claim — 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement.
Published 2026-08-18
Fetched 2026-08-25 10:14 UTC · 435a2ec6edeb1ae4
Torn from — the model card
text-generationtransformersSource Hugging Face
incoai/Qwen3.8-27B-DFlash2174 likes106k downloads
apache-2.0built on Qwen3.8-27B
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-08-18
Fetched 2026-08-25 10:14 UTC · c48c76b964ad9c2d
Torn from — the model card
Pythona2aSource GitHub
wang2122/sprix-sage-router2.0k stars24 forks
MITPythonpushed 4d ago
Location, estimated · Not stated
What we saw
“Sprix AI at 屿智同行 — state-aware SELF/COLLABORATE/HANDOFF routing for A2A agent networks.”
They claim — RouteLLM: Learning to Route LLMs with Preference Data, ICLR 2025 — preference-based cost-quality routing.
Published 2026-08-18
Fetched 2026-08-25 10:14 UTC · 1c90000bebc39c3d
What the account wrote — The account left it blank
Torn from — the README
image-text-to-texttransformersSource Hugging Face
orcarouter/Qwen3.8-27B-Uncensored175 likes15k downloads
apache-2.0built on Qwen3.8-27B
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-08-18
Fetched 2026-08-25 10:14 UTC · 7e63adaac2fe5407
Torn from — the listing only (the original would not open)
JavaScriptagent-orchestrationSource GitHub
Spielewoy/autoprompt-skill845 stars54 forks
MITJavaScriptpushed today
Location, estimated · Unrecognised
What we saw
“Autoprompt is a coding-agent skill that cuts failures by 45% on agentic coding tasks.”
They claim — Autoprompt is a coding-agent skill that cuts failures by 45% on agentic coding tasks.
Published 2026-08-17
Fetched 2026-08-25 10:14 UTC · 5d07e174c6aaeff9
What the account wrote — DE
Torn from — the README
parallel reasoningtemporal confidenceSource HF Daily Papers
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence29 upvotes3 comments
parallel reasoning8 authorsstates limits
What we saw
“ParaTempo improves parallel reasoning efficiency by using temporal confidence to dynamically prune, retire, and reallocate reas…”
They claim — Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining…
Published 2026-08-17
Fetched 2026-08-25 10:14 UTC · 0f7a16969a7c76aa
Torn from — the abstract
Thread ↗ multimodal large language modelshybrid-thinkingSource HF Daily Papers
text-to-image diffusion modelsedit concept granularitySource HF Daily Papers
Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision42 upvotes1 comments
text-to-image diffusion models7 authorsno limits stated
What we saw
“A hierarchical taxonomy and dense supervision strategy improve diffusion-based image editing through fine-grained concepts, lar…”
They claim — To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high…
Published 2026-08-16
Fetched 2026-08-25 10:14 UTC · 38d02ea00a52b522
Torn from — the abstract
Thread ↗ JavaScriptai-agentsSource GitHub
yjh051108/dsh-routing-suite6.8k stars133 forks
MITJavaScriptpushed today
Location, estimated · Unrecognised
What we saw
“dsh-routing-suite — injector + router-standard kit: install the runtime injector first, then the task-aware reasoning-mode ro…”
Published 2026-08-14
Fetched 2026-08-25 10:14 UTC · 5a272e5aab7c2bef
What the account wrote — 天津
Torn from — the README
JavaScriptai-agentsSource GitHub
xmanrui/dsh-im828 stars88 forks
MITJavaScriptpushed today
Location, estimated · Not stated
What we saw
“通过扫码或机器人凭据把IM机器人接入DeepSeek Harness(支持飞书、微信、钉钉、企业微信、QQ、Slack…”
Published 2026-08-14
Fetched 2026-08-25 10:14 UTC · 6e0d29f870534d9f
What the account wrote — The account left it blank
Torn from — the README
text-generationtransformersSource Hugging Face
superwhisper/s1-mini231 likes3.5k downloads
otherbuilt on Qwen3-0.6B
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-08-12
Fetched 2026-08-25 10:14 UTC · cccf20cd52859c98
Torn from — the model card
TypeScriptagentteamsSource GitHub
NanmiCoder/dsh-agent-teams982 stars82 forks
MITTypeScriptpushed 2d ago
Location, estimated · China
What we saw
“AgentTeams plugin for DeepSeek Harness”
Published 2026-08-12
Fetched 2026-08-25 10:14 UTC · ed5787155a705d01
What the account wrote — ShenZhen
Torn from — the README
Source Hacker News
Feature Request: Support AGENTS.md253 points149 comments
no limits stated254 words
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-08-19
Fetched 2026-08-20 07:11 UTC · 0d3b50995f0f64d4
Torn from — the page itself
Thread ↗ Source Google AI
Source OpenAI
Offering Zero Data Retention for frontier modelsAug 19
What we saw
“OpenAI reaffirms Zero Data Retention for eligible…”
Published 2026-08-19
Fetched 2026-08-20 07:11 UTC · 971da21a3ac32a67
Torn from — the listing only (the original would not open)
Nothing on this page matches what you picked.
Papers, repos, models, blogs, Reddit and podcasts — once a day, by machine. Everything collected gets taken apart once — the README, the abstract, the model card — and what that turns up is kept for good, whether or not it becomes a card. Titles and links go to the original; nothing is reproduced here.
The grey line on each card is what the teardown found, not what the source advertises: the licence, the last commit, the model it was built on, whether the paper states its own limits. Open “What we saw” to check what was read to get it. Where it says listing only, the original could not be read — some sites refuse us, and we do not pretend otherwise.
A line in quotation marks is a sentence lifted from the source itself — their number, not a measurement of ours. We copy it and leave it unchecked; that is the point of quoting it.
Each card keeps the sentence we actually read at the moment we read it, plus a fingerprint of the response we received. That sentence — not the whole page — is what we can still stand behind if the original later changes or disappears.
Guessed from one free-text field the account filled in itself, and half of them leave it empty. Shown as its own value rather than hidden.