Convex Social Componentzernio-dev
convex-zernio
A Convex component in TypeScript letting a Convex app schedule and publish social posts through the Zernio API. Submission is durable through a workpool with retries and rate limiting, per-platform status comes back over HMAC-verified webhooks, and a React hook subscribes to it. Profiles are multi-tenant and test mode is the default, so posts land as drafts rather than going live. Apache-2.0, last pushed 30 August, still small at 2 stars.
View repo ↗Idea Reality Checkermnemox-ai
idea-reality-mcp
An MCP server giving a coding agent a reality check on a startup idea by scanning GitHub, Hacker News, npm and PyPI, returning a 0-100 score with competitor evidence and trend direction. Quick mode covers GitHub and HN in under three seconds, deep mode all four. Product Hunt was dropped because its API has no text search. Python, MIT, 813 stars, installed via uvx or Smithery - though the repo has been in maintenance mode since August.
View repo ↗Unified Social APIPostproxy
Postproxy
One publishing and engagement API across Instagram, TikTok, LinkedIn, X, YouTube, Facebook, Threads, Bluesky, Pinterest, Telegram and Google Business, where a single payload handles per-platform formatting alongside comment, DM and review management, webhooks and status logging. Node, Python and Ruby SDKs, a sandbox, verified n8n, Make and Zapier integrations, and an MCP server for agent workflows. States 35ms average response, EU-hosted, SOC 2 Type II in progress; free tier, no card.
View release ↗Agent Skill ScannerNVIDIA
SkillSpector v2.11.0
Since Week 24 2026: a security scanner for agent skills across Claude Code, Codex CLI, Gemini CLI and MCP, matching 71 vulnerability patterns in 17 categories - prompt injection, data exfiltration, privilege escalation, supply-chain risk - through fast static scanning, an optional LLM semantic pass, live OSV.dev CVE lookups and a 0-100 risk score. It was built on an empirical study of 42,447 skills that found 26.1% carrying vulnerabilities and 5.2% showing likely malicious intent. Apache-2.0, 16.3k stars, v2.11.0 tagged 28 August.
View repo ↗Codebase Diagram Generatortt-a1i
Archify v2.16.0
An agent skill for Cursor, Claude Code, Codex CLI and OpenCode turning a codebase or a written system description into an interactive system map - architecture, workflow, sequence, data-flow and lifecycle diagrams - rendered as self-contained HTML and SVG through a typed JSON intermediate representation. Includes an Architecture Delta view diffing before and after, route tracing, and export to PNG, SVG or WebM. MIT, 49.6k stars, v2.16.0 tagged 30 August.
View repo ↗Peer-to-Peer InferenceSwarmLLM
SwarmLLM
Browser-based peer-to-peer inference splitting one model across every device in a room so they act as a single compute unit, running a model no single device could hold. Devices join on a four-letter room code with no install, account or server; each downloads a slice sized to its capability and the response is computed collectively and appears on every screen at once. The site names Qwen 3.8 as the model and argues the privacy case that nothing leaves the room. No organisation, version or pricing appears anywhere on the page.
View release ↗GPU Architecture GuideDr Ashish Bamania
NVIDIA GPUs for AI Engineers
A free explainer on the GPU hardware an AI engineer actually has to reason about: streaming multiprocessors, CUDA and tensor cores, on-chip SRAM against HBM, and the NVIDIA line from Volta and the V100 in 2017 through T4, A100, H100, H200, L40S, B100, B200, B300 and Rubin to Feynman in 2028. It then covers interconnects - PCIe, NVLink 4 to 6, NVSwitch, NVLink-C2C - and scales outward through Spectrum-X Ethernet and Quantum InfiniBand to the node, rack, cluster and facility hierarchy, mapping tensor, data and pipeline parallelism onto it.
View release ↗GPU Concepts PrimerDr Ashish Bamania
GPU Concepts for AI Engineers
Nine GPU concepts in four groups: computation hardware covering streaming multiprocessors, CUDA cores across INT32, FP32 and FP64 and tensor cores doing matrix multiply-accumulate; GPU memory with HBM holding weights and activations above an on-chip hierarchy; program execution; and multi-GPU connectivity. The framing is debugging - working out where the compute budget actually goes when a model runs slower than expected. The first four concepts are free to read, five through nine sit behind the paywall.
View release ↗LLM Inference InternalsDr Ashish Bamania
A Hardware-Level Tour of LLM Inference
Traces inference down to the metal: weights moving from SSD through CPU DRAM into GPU HBM over PCIe, the CPU-HBM-SRAM hierarchy, and the tensor, pipeline, context, expert and data parallelism used to spread work across GPUs. It separates the compute-bound prefill phase, where the whole prompt is processed at once, from the memory-bound decode phase producing one token at a time, and explains the KV cache as what prevents the recomputation. The argument it builds to is that memory bandwidth, not raw compute, is the wall.
View release ↗Distributed Data ParallelDr Ashish Bamania
Distributed Data Parallel Explained
A free, diagram-led walk through PyTorch DDP in eleven steps: replicating the model per GPU, splitting the dataset with DistributedSampler, the forward and backward passes, and gradient synchronisation through AllReduce, plus the optimisation overlapping gradient computation with communication. A worked three-GPU example carries it. It closes on the limit that motivates model parallelism - an 8B Llama needs roughly 151GB, more than an 80GB H100 holds. Billed as Part 1, with the code in Part 2.
View release ↗PyTorch DDP WalkthroughDr Ashish Bamania
Train a CNN with PyTorch DDP
The hands-on Part 2 to the DDP explainer: training a CNN image classifier across several NVIDIA T4 GPUs on Kaggle with PyTorch DistributedDataParallel, over CIFAR-10's 60,000 32x32 images in ten classes. It walks the imports for torch.distributed and TorchVision, then a three-block convolutional architecture with batch normalisation, pooling and fully connected classification, plus the scaffolding for the distributed helper functions. Marked paid, so only the preview is readable without a subscription.
View release ↗Llama Distributed TrainingDr Ashish Bamania
Distributed Training of Llama Explained
How Meta trains at Llama scale using 4D parallelism: FSDP sharding parameters, optimiser states and gradients rather than replicating them; context parallelism splitting 128K+ token sequences; tensor parallelism splitting weights along the hidden dimension; and pipeline parallelism splitting layers into stages fed by micro-batches, with expert parallelism as a fifth dimension for MoE. Grounded in Llama 3-405B's real run - 16,000 GPUs at 80GB HBM3, 54 days, 30.84M GPU-hours - and it gives the arrangement priority order as TP, CP, PP then DP and FSDP, ranked by communication bandwidth.
View release ↗Agent Visualiserliuyixin-louis
AgentRoom
A Tauri desktop app - Rust backend, React and Canvas 2D front end - drawing your running coding agents as pixel-art characters in a virtual office, with a work room for active agents and a break room for idle ones. It tails the JSONL transcripts Claude Code, Codex and Gemini write and turns them into events driving character state and pathfinding, alongside full-text session search, per-project offices, a token-usage dashboard, AI session tagging and one-click iTerm2 integration. MIT. Not the same project as the similarly named agent-room, despite the collision.
View repo ↗Free Market Data ArchiveLondon Strategic Edge
London Strategic Edge
Not the consultancy the domain suggests: a free financial-data platform hosting what it calls the largest free market-data archive, 133 billion ticks across 118,000 datasets covering stocks, options, forex, crypto, futures and macro series back to 1900 for over 100 countries. It ships working tools on top - strategy backtesting, LSTM and XGBoost forecasting, live charts, options analysis and screening across 16,000+ instruments - with history downloadable as Parquet or CSV through a free API key, no paywall and no card.
View release ↗Engineering Workflow PluginLauren Tan
pstack
A Cursor plugin, with an unofficial Claude Code port, expanding a short request into a structured engineering workflow - 23 workflow skills, 21 engineering principles, 22 task playbooks and specialised subagents - entered through a single /poteto-mode command. The skills use the standard SKILL.md format so they carry across Claude Code, Codex and other agents, and sub-tasks can be routed to different models. Distributed through Cursor's official plugin system.
View release ↗Self-Hosted Financesecuro-finance
Securo
Self-hosted personal finance manager on FastAPI, SQLAlchemy, Alembic and Celery with a React and Vite front end over PostgreSQL and Redis. Handles multi-account and multi-currency tracking, file import, auto-categorisation rules, budgets and savings goals, bank sync through Pluggy, Enable Banking and SimpleFIN, and login by 2FA, OIDC or passkey, with optional self-hosted AI agents that can call tools. AGPL-3.0, 3.1k stars.
View repo ↗Debugger MCP Serverwasdubya
x64dbgMCP
An MCP server in C++ and Python wiring an LLM into the x64dbg debugger so reverse engineering can be driven in natural language, exposing over 40 SDK functions - register queries, byte-pattern search and the rest - across 32-bit and 64-bit targets. GPL-3.0, 517 stars. The debugger underneath is a decade-old project with nothing new this cycle; the MCP layer is what is new.
View repo ↗AI Slide DecksStackBlitz
Bolt Slides
Lets an agent generate an interactive presentation from one prompt, where every slide is a live React component rather than an image - so a slide can hold real data, 3D elements or a working prototype. Ships presenter mode with synced tabs, annotations and deep linking, more than 20 prebuilt slide components covering charts, quotes and pricing layouts, CSS-token theming, and a bundled skill guide telling an agent how to compose them. TypeScript and Vite, MIT, 924 stars.
View repo ↗Local Model Servermagnitudedev
Magnitude
A local inference server that profiles the machine it is on, recommends and downloads models that will actually fit, then plugs them into existing coding agents including Pi, OpenCode, Hermes and Cline so they run free, private and offline. Models load just in time and unload when idle. TypeScript and Bun, Apache-2.0, on macOS, Linux and Windows through WSL, installed with npm i -g @magnitudedev/cli. 3.3k stars.
View repo ↗Order Routing Platformopenshiporg
Openship
Order routing between sales channels - Shopify, WooCommerce, eBay, Amazon - and fulfilment partners including supplier stores, 3PLs, email and Google Sheets, modelled as shops, channels, links and product-level matches so orders route automatically. Next.js 15 and KeystoneJS 6 behind a GraphQL API over PostgreSQL and Prisma. AGPL-3.0, 1.6k stars. E-commerce infrastructure rather than an AI release.
View repo ↗Stickman Prompt Skillkaomei
Stickman Video Director
A Codex skill rather than a model: it turns an article or piece of copy into a complete one-minute stickman animation production package for Gemini Omni Flash - confirmed narration, a directorial proposal, and six independent prompts carrying timing beats, camera moves, transitions and sound design. Light and dark themes, 9:16, 16:9 and 1:1. MIT, 769 stars. It writes the prompts; another model renders the video.
View repo ↗Multivariate ForecastingGoogle Research
TimesFM-3
A 330M decoder-only transformer for time-series forecasting pretrained on over a trillion time points, and the first in the line that is natively multivariate - where TimesFM and 2.5 were strictly univariate, this handles multiple targets, past covariates and past-future dynamic covariates in one forward pass. Uses 32-step patches, alternating causal temporal and full variate attention, and non-autoregressive contiguous patch masking to emit nine quantiles per target in a single pass. Tops Gift-Eval, FEV-Bench and TIME against Chronos-2, Toto 2.0 and TimesFM-2.5 on both point and probabilistic metrics; code and weights released, BigQuery integration promised.
View release ↗Video Delta AttentionUC Berkeley, Impossible and UT Austin
VDN
Video DeltaNet is a hybrid attention design pairing sliding-window softmax attention with a linear-attention branch, aimed at the more than 85% of runtime softmax attention eats in video diffusion, without the identity, layout and temporal-consistency damage linear attention usually brings. Applied to MiniMax H3 the authors report 2.65x per layer - 125.3ms against 332.5ms on one B200 - a 14.3-second 768p clip in 11.23 seconds on eight B200s, and up to 74.5x against a dense single-GPU baseline. Code and weights released.
View project ↗VDN ComfyUI NodeSaganaki22
ComfyUI-VDN-H3
Ports VDN into ComfyUI for MiniMax-H3, patching attention at runtime - exact softmax for nearby frames, a constant-cost recurrent linear branch for distant temporal context - without touching ComfyUI core. Needs the H3 base weights plus a Qwen3VL-32B text encoder and separate video and audio VAEs. On a single RTX 5090 at int8, 1280x736 and 145 frames it runs about 17 seconds an iteration, roughly two and a quarter minutes total; the paper's headline 74.5x needs eight GPUs and kernels consumer hardware does not have. The node is Apache-2.0, the weights gated under the MiniMax H3 community licence.
View repo ↗