0:43Ming Image-0.1 Design, Reviewed: Built for Real Design Pipelines
@realfxw · 134 views · 2026-09-23
Image GenerationText RenderingUI Design
[●●●]LOADING
AGIENTRY INTELLIGENCE
Footprints on the Road to AGI
One job: collect what has actually happened on the road to AGI, with every entry traceable to a paper, a repo, a benchmark or a video. No headline chasing and no full tool directory — what shows here is only what we read ourselves.
Demos
1410:43
1:02GLIDE: Guardrails Make Infeasible Teleoperation Learnable
@stepjamUK · 116 views · 2026-09-23
TeleoperationGuardrailsImitation Learning
0:51GAE: A Geometry-Native Latent Space for 3D-Consistent World Generation
@HuggingPapers · 136 views · 2026-09-23
World Models3D GenerationLatent Space
0:58Tripo SmartMesh P2.0: One-Click Auto UV Unwrap Tested
@hos_giken · 130 views · 2026-09-20
3D ModelingUV UnwrappingTripo
0:23Meshy V7.1 Now Live on fal: Text or Image to Production-Ready 3D
@fal · 99 views · 2026-09-21
Text-to-3DMeshyPBR
2:19NetEase Youdao R2T2 + T3PO: Streaming ASR and Translation, Tested Live
@MinLiBuilds · 96 views · 2026-09-23
Speech RecognitionMachine TranslationStreaming
2:53WorldCrafter: Video World Exploration Without 3D Reconstruction
@TencentAI_News · 90 views · 2026-09-23
World ModelsVideo GenerationTencent
0:39Yu-Gi-Oh AR Duels Prototyped on Snap SPECS Glasses
@tokufxug · 60 views · 2026-09-23
Augmented RealitySnap SpecsLens Studio
LLM
12ReasoningDevelopmentTopA
GPT-6 Astra: first on terminal engineering tasks, third tier on composite intelligence
The OpenAI flagship tier, officially positioned for hardest end-to-end work (gpt-6-astra): 1.05M-token context, 128K-token single output, knowledge cutoff 2026-04-30, with Functions, Web search, File search and Computer use built in, so search, retrieval and interface control ship with the model instead of an outer agent framework. Reasoning effort runs low to max in five steps while price stays fixed at $10/$50, so the cost lever is token consumption; siblings Sol ($2/$10) and Luna ($0.1/$0.5) make a 100x spread, so load splitting can stay inside one vendor. Readings (2026-09-22): #1 on Terminal-Bench 4.0 at 58.18% (n_trials=330, pass@5 0.7121), the protocol closest to a coding agent's daily life; #2 on Arena Agent with net_improvement 11.54 and confirmed_success 17.70, both below leader Fable 5.1, but the field's highest praise at 32.79; #6 and #7 on the intelligence index (max 52.67). The cutoff is nearly five months older than this page, so version numbers and API changes must go through Web search; computer-use reliability appears on no board; closed, API only. Graded A (confirmed); not benchmarked by us.
58.18%Terminal-Bench 准确率Confirmed · 2026-09
GPT-6 Astra: first on terminal engineering tasks, third tier on composite intelligenceReasoningDevelopmentTopA
Claude Fable 5.1: the long-horizon agentic tier that tops the ladder on a different ruler
The fixed version Anthropic positions for demanding reasoning and long-horizon agentic work (claude-fable-5-1, retirement no earlier than 2027-09-01): default thinking effort high at $10/$50 per million tokens, 2.5x sibling Opus 5.5, and default-high is itself a default cost behaviour. Its standing only holds once you switch rulers: #1 on net_improvement in the Arena Agent board (13.71, confirmed_success 19.83, praise 31.83) ahead of GPT-6 Astra (11.54/17.70/32.79), while Opus 5.5 misses the top eight; #2 on Terminal-Bench 4.0 at 57.88% (n=330), 0.3 points behind Astra at 58.18% but with pass@5 of 0.7879 against 0.7121, lower single-shot and more robust over five attempts; #4 on the intelligence index at 53.35 (max), below Opus 5.5. The picture is consistent: not first on composite intelligence, first on pushing a real task forward. A separate praise column means the board carries a human or judge component and is not an objective benchmark. Choose by whether the workload is hard from reasoning or from long-horizon consistency. Closed, API only. Graded A (confirmed); no long-task comparison on our own harness.
13.71Arena Agent 净改进Confirmed · 2026-09
Claude Fable 5.1: the long-horizon agentic tier that tops the ladder on a different rulerReasoningDevelopmentTopA
Claude Opus 5.5: the long-running agentic tier at $4/$20 with 1M context and adaptive thinking
The fixed version Anthropic positions for long-running agentic coding and knowledge work (claude-opus-5-5, retirement no earlier than 2027-09-22): 1M-token context, 128K-token single output, adaptive thinking defaulting to medium, at $4/$20 per million tokens, the cheapest of the three top rows we track. On the Artificial Analysis intelligence index this site syncs (2026-09-22) the top three rows are all its thinking tiers (max 57.62, xhigh 55.99, high 53.58), while the default medium tier sits #8 at 51.24, with sibling Fable 5.1 (53.35) and GPT-6 Astra (52.67) in between. Two discounts: those rows carry the with fallback qualifier while Astra reads (max), so the protocols differ, and the 53.94% at #8 on Terminal-Bench belongs to the previous-generation Opus 5. Closed, API only; adaptive thinking is a black box, so budget on p90 rather than the mean; 1M context is not 1M of effective attention, so whole-repo input still needs retrieval. Use it for most workloads and move to Fable 5.1 only when the highest tier is not enough: 2.5x the price for 4.3 index points. Graded A (confirmed); not benchmarked by us.
57.62AA 智能指数Confirmed · 2026-09
Claude Opus 5.5: the long-running agentic tier at $4/$20 with 1M context and adaptive thinking
Harness
18The runtime an agent stands on: plugin and tool surface, sandbox and permission boundaries, skill systems, long-term memory and self-improvement loops. Model capability only lands through that scaffold, so it gets its own line here.
Skills
13Developmentobra/superpowersFeatured
Fifteen skills that give a coding agent a full development process
Our takeIts premise is that models lack process discipline, not coding ability. The single best idea to steal is 'Rulings, not stalls': decide by default, and stop for a human only when an action is irreversible, touches a security-sensitive surface, leaks outside the worktree, or the plan is already too broken to continue. We adopted those four gates in our own pipelines.
v6.4.1 (2026-09-18), MIT, 289k stars — the largest single skill repository we have found. It chains brainstorming, spec writing, git-worktree isolation, planning, subagent execution, red-green TDD, two-way code review and verification-before-completion into one trunk workflow, and the README lists 16+ harnesses: Claude Code, Codex, Cursor, Gemini CLI, OpenCode, Kimi Code, Devin CLI and more.
Claude CodeCodexCursor
/plugin install superpowers@claude-plugins-officialShellobra289.2k
Fifteen skills that give a coding agent a full development processDevelopmentmattpocock/skillsFeatured
The 38 skills Matt Pocock uses every day for real engineering
Our takeIt sells parts rather than a whole process: code-review, diagnosing-bugs and resolving-merge-conflicts each stand alone. grill-me, grilling and wait-what turn the agent around to interrogate your requirements, and nothing else in this batch does that. CONTEXT.md at the repo root decouples project vocabulary from generic skills — the cleanest version of that idea we have seen.
The personal working set of the author of Total TypeScript; the repo describes itself as real engineering, not vibe coding. 38 SKILL.md files, primary language Shell, grouped as engineering (18), productivity (7), misc (4) and in-progress (9). It explicitly rejects takeover-style frameworks such as GSD, BMAD and Spec-Kit in favour of small, easy to adapt, composable.
Claude Code
claude plugins install mattpocock-skillsShellmattpocock266.4k
The 38 skills Matt Pocock uses every day for real engineeringOfficial & Collectionsanthropics/skillsFeatured
The official repo from the format's author: 19 skills plus spec and template
Our takeIf you want to know how a SKILL.md is actually supposed to be written, read this instead of any second-hand tutorial. Two things to know first: docx, pdf, pptx and xlsx are source-available rather than open source (the other 15 are Apache-2.0), and the repo carries its own disclaimer — these skills are demos, so test them in your environment before trusting them with anything critical.
Anthropic's own implementation, 177k stars: 19 skills under ./skills plus ./spec (the Agent Skills specification, now at agentskills.io) and ./template as a scaffold. Its definition of a skill is folders of instructions, scripts and resources loaded dynamically, and progressive disclosure comes straight from here.
Claude Code
/plugin marketplace add anthropics/skillsPythonanthropics177.3k
The official repo from the format's author: 19 skills plus spec and templateDesign & Frontendnextlevelbuilder/ui-ux-pro-max-skillFeatured
Design knowledge as searchable local data: 79 styles, 192 palettes, 119 UX rules
Our takeIts sharpest self-description is that models can write CSS but lack a taste database. Ten rule categories are ordered by severity, with Accessibility and Touch & Interaction first as CRITICAL and Charts last — that ordering is the stance. The Design System Generator runs five parallel searches, and the repo states plainly: do not persist unverified output, so a generated design system has to be checked before it lands in your repo.
v2.13.0, MIT, 129k stars, site uupm.cc. Instead of lecturing the model it ships structured catalogues the agent queries before writing code: 79 UI styles, 192 palettes with reasoning, 74 type pairings, 119 UX rules, 105 icon suggestions, 17 GSAP presets, 25 chart types, 22 stacks and 34 landing-page patterns.
Any agent
npx ui-ux-pro-max-cli init --ai claudePythonnextlevelbuilder129.3k
Design knowledge as searchable local data: 79 styles, 192 palettes, 119 UX rulesKnowledgeGraphify-Labs/graphifyFeatured
Map the whole project into a queryable knowledge graph instead of grepping
Our takeThe design that matters most: every edge is labelled EXTRACTED or INFERRED, so you can tell what the source explicitly contains from what the tool inferred — which makes an agent's conclusions auditable, a hard requirement in production. It is not a vector index: no embeddings, no vector store, a real graph you can explain, path and query. In its own benchmark, building the graph costs zero LLM spend and ingest is an order of magnitude cheaper, a structural win from local parsing.
YC S26, Apache-2.0, 120k stars, Python 3.10+. /graphify . maps code, docs, PDFs, images and videos into a graph and emits a clickable graph.html, a human-readable GRAPH_REPORT.md and a machine-readable graph.json. Code goes through tree-sitter AST parsing (about 40 languages): deterministic, no LLM calls, nothing leaves the machine.
Claude CodeCodexCursor
uv tool install graphifyy && graphify installPythonGraphify-Labs119.9k
Map the whole project into a queryable knowledge graph instead of greppingOfficial & CollectionsComposioHQ/awesome-claude-skillsFeatured
A curated index of 1000+ skills, plus one genuinely unique connect-apps plugin
Our takeThere is no one-liner that installs this repo; use it as a discovery layer. A list answers what exists, not what is worth running. The part you cannot get elsewhere is connect-apps-plugin: a single MCP endpoint to 1000+ integrations with authentication, team-level ACLs and audit logs, which is exactly the governance problem enterprises hit when agents touch SaaS.
A list maintained by Composio at 75k stars with an Apache-2.0 badge in the README. About 31 first-party skill directories physically exist here while the index references 864 SKILL.md files, and most of the difference is links out to external repositories. Coverage explicitly extends beyond Claude.ai and Claude Code to Codex, Cursor, Gemini CLI and Antigravity.
Any agent
claude --plugin-dir ./connect-apps-pluginPythonComposioHQ75.4k
A curated index of 1000+ skills, plus one genuinely unique connect-apps pluginLeaderboard
469Ranks come verbatim from each evaluator's own board, synced into our database on a schedule. We never recompute, reweight or merge across sources: an intelligence index, an Elo and a success rate are different rulers, and any single overall number would be incomparable figures stitched into one that looks comparable.
Artificial Analysis Intelligence IndexOfficial intelligence index (higher is better)
Claude Opus 5.5 (max with fallback)Anthropic57.62
Claude Opus 5.5 (xhigh with fallback)Anthropic55.99
Claude Opus 5.5 (high with fallback)Anthropic53.58
Claude Fable 5.1 (max with fallback)Anthropic53.35
Claude Fable 5.1 (xhigh with fallback)Anthropic53.2
Source · artificialanalysis.aiofficial Intelligence Index v4.3synced Sep 22, 2026
Best in each other domainTop row of each board; the rulers differ
AgentsClaude Fable 5.1 (Max)13.71%CodeSonar Foundation Agent + Claude 4.5 Opus79.2%Imagegpt-image-2.5-sunburst1423Videogemini-omni-1.1-flash15163DProlificDreamer43.3EmbodiedDM0.524.9Capabilities & verticals
5Two axes of AIGC: the horizontal one is model capability (image, video, 3D and world models), the vertical one is which workflow it lands in (design, office). The five topbar entries live in this row.
Image Generationtracked 13
Text-to-image, image editing, consistency and controllability
GPT Image 2.5: the image flagship that writes multi-round editing consistency into the API contract1423/1526CQwen-Image-2.1: one set of open weights for generation, local editing and transparency7BCFLUX: the line that moved image generation from "produce a picture" to controllable editing with open weightsApache-2.0C
GenAI-Bench, DrawBench, arena preference, controllability
Image GenerationVideo Generationtracked 5
Text/image-to-video, physical and temporal coherence
Gemini Omni: moving the unit of video delivery from one attempt to a conversational editing process1516AKling 3.0 series: native audio, 4K frames, and parameterised camera language as the moat3.0 / Omni / TurboCSeedance 2.5: raising the unit of video delivery from one shot to a 30-second story beat30sC
VBench, EvalCrafter, physical & temporal coherence
Video Generation3D & World Modelstracked 10
3D asset generation, scene reconstruction, long-horizon world prediction
Meshy 7: the 3D asset platform that finishes what comes after generation, from topology and PBR to rigging, printability, and MCP<1minCTripo: image-to-engine-ready-asset compressed into a ten-second step<10sCHunyuan3D 2.1: the open-weight 3D generation baseline and default starting point for self-hostingC
Geometric consistency, scene reconstruction, long-horizon prediction
3D & World ModelsDesign & Frontend1 skills · 7 assets
Visual design, UI components, web and slide layout
Design & FrontendDocs & Slides2 skills · 1 assets
Slides, Word, Excel, PDF generation and editing
Docs & SlidesBlog
59
Laya, the Open-Source System 1 Decision Model: 421M Parameters, One 33 ms Forward Pass
Convai Innovations' open answer to TypeSafe Jev: a 421M non-autoregressive decision model trained with RLCD proper scoring rules, plus the README's honest limits and real Jev comparisons.
System One ModelsLayaRLCDNon-AutoregressiveModel Calibration
How to Build a Humanoid Robot at Home: The Asimov 1 Build Guide
Asimov opens up its $20k DIY humanoid kit: 25-DoF hardware, fabrication codes, CAN wiring traps, and the 200 Hz sim / 50 Hz asymmetric actor-critic behind zero-fine-tuning sim-to-real walking.
HumanoidDIY RoboticsSim-to-RealReinforcement LearningLegged Locomotion
Code2Skill: Mining a Million Grounded Agent Skills from 19,769 GitHub Repositories
Code2Skill turns 19,769 GitHub repos into 1,006,822 verified skill records; retrieved skills lift macro-average 11.7% and beat trajectory-derived banks on all seven shared benchmarks.
Agent SkillsCode2SkillCodeSkillBankSkill SynthesisLLM Agent
AI Games
2One admission rule: the opposition in a match is driven by live decisions, not a scripted performance. Click a card and the round starts in-page, same-origin, nothing to install. Each card states its own verifiable claim - where the decisions come from, whether the scene is rebuilt or read out of its source, and what runs on a fixed step.
ThreeDream FPS arena
A first-person match where every bot across from you is driven live by a policy this repo trained, the whole round stepping at a fixed 60 Hz: builtin, Rust/wasm or Rapier behind one physics interface, nothing scripted.
WASD move · mouse look · left-click fire · shift sprint · R reload · esc release
ThreeDream UE level
Unreal 5.5's first-person test map, running same-origin on this site: the level, the materials, the lights and four weapon data assets are read out of the project's own .umap / .uasset bytes, six soldiers are driven by a four-state brain on one fixed 60 Hz step, and the sidebar lists every substitution the page had to make.
WASD move · mouse look · left-click fire · shift sprint · space jump · R reload · F pick up · esc release