EveryDev AI

Shipping AI agents without knowing how they fail under attack or what data they might leak? Enkrypt AI is an agent security and compliance platform. It red teams agents, enforces runtime guardrails and policies, and scans MCP servers and routes traffic through an MCP gateway. Freemium, with...

Pasting stack traces into chat and hoping the patch works, with no idea what each call costs? Inferiq from is an OpenAI-compatible gateway with fix agents (Quick Fix, Build Doctor) that return a cause and a patch. You get a quote with a hard maximum before each run and a receipt after. Works...

Stitching together RAG pipelines, UI kits, file transfer and monitoring from separate vendors gets messy fast. Progress Software bundles AI infrastructure with Agentic RAG-as-a-Service for agents, plus Kendo UI, Telerik UI, Test Studio, Fiddler, Chef and MOVEit. From, it has a freemium model...

1/ Two friction points in coding agents right now: permission babysitting, and long sessions that stall when context fills up.

github.blog/changelog/2... Ask-everything vs approve-everything vs assisted — which mode can your team live with? And if your agents run long enough to fill context, the compact-and-continue path is the practical one.

1/ Two friction points in coding agents right now: permission babysitting, and long sessions that stall when context fills up.

Claude Code v2.1.311: standard-context sessions no longer hard-stop past 200K tokens asking for 1M-context credits. They compact and continue. Also fixes hang-retry loops and a subagent worktree deleted while a background agent still used it.

1/ Two friction points in coding agents right now: permission babysitting, and long sessions that stall when context fills up.

Running a vector database for semantic search or RAG usually means provisioning servers and managing indexes. Upstash Vector from is a serverless, fully managed option with DiskANN-based search, metadata filtering, namespaces, hybrid and sparse indexes, and built-in embedding models. It has a...

Models too big or too slow to run on the GPU memory you have? TorchAO from quantizes and sparsifies models for both training and inference, natively in PyTorch. It supports int4, int8 and float8 quantization, QAT, float8 training, and works with torch.compile and FSDP2. It's open source and...

Prompts buried in app code are hard to review, test, or trace. Prompty from puts each prompt in a markdown.prompty file with YAML frontmatter, Jinja2 or Mustache templates, typed inputs, and function, MCP, or OpenAPI tools. It adds an agentic turn() runtime, OpenTelemetry tracing, and a VS Code...

Running coding agents on your laptop ties up your machine and risks your local environment. Open Agents from is an open-source reference app for running background coding agents in isolated cloud sandboxes. It clones repos, works on branches, and can auto-commit, push and open PRs. Built on...

Downloading a 500GB dataset just to test a training loop? Hugging Face Datasets from loads data in one line with load_dataset() and can stream it without a full download. It uses Apache Arrow memory-mapping, caches processed results, and converts to PyTorch, TensorFlow, JAX, Pandas, Polars and...

1/ $11.43 vs $0.39: stop using your best model to rename props. Those two figures are Cursor's published CursorBench cost-per-task for Opus 5 Extra High ($11.43) vs GLM 5.3 Flash Max ($0.39) — ambiguous multi-file agent tasks in Cursor's harness, not EveryDev rename timings.

4. Is this merge-blocking architecture? → frontier, then human review. Illustrative list-rate math (not a measured run) — ~20k input + 500 output tokens: • Sonnet 5.5 @ $2/$10 per 1M ≈ $0.04 + $0.005 ≈ $0.045 • DeepSeek V4.1 Flash off-peak miss @ $0.15/$0.60 ≈ $0.003 + $0.0003 ≈ $0.003

1/ $11.43 vs $0.39: stop using your best model to rename props. Those two figures are Cursor's published CursorBench cost-per-task for Opus 5 Extra High ($11.43) vs GLM 5.3 Flash Max ($0.39) — ambiguous multi-file agent tasks in Cursor's harness, not EveryDev rename timings.

• Multi-file design change with real blast radius • Debugging that needs cross-repo reasoning • Security-sensitive review, migrations, "one wrong edit = outage" Cursor's own routing writeup makes the same point: classify the request before you burn the expensive model.

1/ $11.43 vs $0.39: stop using your best model to rename props. Those two figures are Cursor's published CursorBench cost-per-task for Opus 5 Extra High ($11.43) vs GLM 5.3 Flash Max ($0.39) — ambiguous multi-file agent tasks in Cursor's harness, not EveryDev rename timings.

Your day isn't one benchmark row. But if your default model is always the expensive column, you're paying frontier rates for work a flash model can finish.

Can't tell which AI systems your teams have shipped, or whether they'd pass an audit? Holistic AI discovers shadow AI, builds an inventory with owners and risk levels, red teams for prompt injection and jailbreaks, and monitors drift in production. It's paid, via contact sales, and integrates...

Hard to know if your robot manipulation policy generalizes when every lab tests on its own tasks. RoboLab runs 120 language-instructed manipulation tasks in simulation on NVIDIA Isaac Lab, with automatic success detection, scene variations for lighting, cameras and textures, and a results...

Can't say which AI systems your company runs, who approved them, or what a failure would cost? Modulos from discovers AI systems, maps one control library to multiple regulatory frameworks, quantifies risk in monetary terms, and tests live systems against policies. Paid with contact-sales...

Running OpenClaw agents from the command line means juggling configs, sessions and schedules by hand. ClawX is a desktop app from that puts a GUI on top: setup wizard, multi-session chat, agent lifecycle controls and cron-based automation. Keys stay in your native keychain. It's MIT open...

Your coding agent forgets everything when the session ends, so you keep re-explaining the same project context. memsearch from adds persistent memory across sessions. Notes live as Markdown files, with a rebuildable Milvus index using dense vectors, BM25 and RRF reranking. It works with Claude...

Plain vector search often fails when an LLM has to answer questions about a whole document set, not just one passage. GraphRAG from extracts a knowledge graph from unstructured text, clusters it with hierarchical Leiden communities, and builds summaries bottom-up. It offers Global, Local, DRIFT...

Your agent figures something out in one run, then forgets it in the next. Acontext from captures what agents learn from sessions and distills it into readable Markdown skill files you can inspect, edit, and export as ZIP. It's open source and self-hosted via acontext-cli, with Python and...

Serving a big LLM costs too much GPU memory, and quantizing it by hand is slow and fiddly. LLM Compressor from is an open source Python library that quantizes and prunes models in one oneshot call, using GPTQ, AWQ, SmoothQuant and more. It saves compressed-tensors checkpoints that load directly...

Most “build an agent” tutorials are prompt plumbing. The useful skill is harness engineering: the loop, tools, permissions, and context file that let a model act in a real repo. A working coding-agent harness fits in roughly 200 lines:

1. shareAI-lab/learn-claude-code — progressive “model + harness” course (canonical home of the mini Claude Code tutorial; ~79k stars). s01 is the bare agent loop (~150 lines). www.everydev.ai/tools/learn...

Most “build an agent” tutorials are prompt plumbing. The useful skill is harness engineering: the loop, tools, permissions, and context file that let a model act in a real repo. A working coding-agent harness fits in roughly 200 lines: