v0.13.0: 1- Full link support: reference data from the url, request/response bodies, query params, etc. and compose it by itself or concatenated. 2- baseUrl option to easily change the url paths to streamline testing and development. 3- Embracing GraphQL conventions even better

11 GitHub repos that let Claude Code use Photoshop, Word, Excel, CapCut, Blender and more, for free. Each one is an open-source app or plugin an AI agent can operate directly. 1. Photoshop → PhotoCraft, an open-source clone with its own MCP server 2. Word → WordCraft, opens .docx files and has an MCP server built in 3. Excel → GridCraft, reads and writes .xlsx through MCP 4. CapCut → Concat, a video editor with an MCP API 5. AutoCAD and SolidWorks → text-to-cad, outputs STEP, STL and 3MF files 6. Blender → MCP for Blender, a community-built connector 7. After Effects → HyperFrames, turns HTML into MP4 videos 8. ElevenLabs → VoiceStudio, local voice cloning with an API and MCP 9. Visio and Lucidchart → diagram-design, 45 diagram types 10. Chrome → Playwright MCP, lets the agent use a real browser 11. Everything else → CLI-Anything, makes any app agent-ready The loop: prompt → design → write → crunch → edit → render → ship

AGENTIC TRAFFIC NOW MAKES UP MORE THAN 70% OF ALL INFERENCE TRAFFIC 🚀 Agentic workloads are characterized by four elements: 🟠 Multi-turn: a session includes tens or hundreds of turns, leading to high potential KV-cache reuse. 🟠 Long context: system prompts, tool definitions, and the large number of turns make context accumulate quickly. 🟠 High prefix reuse: since the conversation progresses linearly, where output from turn n-1 is concatenated to turn n (typically), most context can be served from KV cache rather than recomputed (this depends on the amount of storage available to store KV tensors). As n grows, the ratio of cached input relative to uncached input typically tends towards 1. 🟠 Sub-agent bursts: a session launches multiple short-lived sub-agents with fresh context, which create bursty KV-cache patterns.

Introducing FlyOCR 🪰 - I trained a fly brain to read a PDF It uses the full MaleCNS v1.0 fruit fly connectcome. The architecture is inspired by doomfly by @wormuth The fly splits a pdf image into individual glyphs, maps pixels into receptor activations, runs simplified current-based dynamics across the 166k neurons and 25m edges in the circuit, applies a compact readout model on the downstream spikes, and concatenates everything into the parsed output. On reading an actual Microsoft 10-k, the fly gets ~86% over the balance sheet heading, but is largely able to read the numeric values correctly. Over 1.7k+ sampled glyphs (chars+digits) it gets 87% accuracy. With enough training it might match some of the latter-generation MNIST models! Maybe eventually we’ll replace our doc parsing VLMs with flies. Full video below. Repo with full code + report:

fyi we had around ~10k new launches with the new pre-IPO feature release. just imagine what would've happened if we didn't enforce ticker locks + client limits on new launches. we would've probably seen 100k launches within a single day. this is where a platform becomes a product used by bots rather than real humans, who can't possibly filter through so many new assets. this ends up driving FOMO trades and chasing volume, which is not hard to inflate. now also imagine what might be the effect of these launch limits constantly being tweaked with more demand coming in, esp once LONG will start scale very fast(hint concatenation of liquidity + flows into the best and most unique assets in the eco) we are going to win so much that you'll get tired of winning. next steps: 1. I want to apply filters that help users discover assets based on a variety of params (whale %, age, anti-fragility and some surprises). I think this is something we can innovate on as well. just better discovery. 2. we are actively monitoring any suspicious activity across all pairs. as I said, it's not hard to tweak our params. I don't see anything critical as of now, but we're already preparing some new params we can execute on with a click of a button, as soon as we feel like PNDs are becoming coordinated rather than organic market activity(remember AI nuked by 80%+ like 4 times) 3. As always, I think the best trading style on LONG is to find a team/project you think can cook and grow out of bad periods, help them out, and build a nice Longfolio of PVE assets. Keep in mind all of our top pairs were able to break ATHs or sustain value weeks post launch. LONG.

🚨 On September 6, 2026, @Liquid_BTC was affected by a cache key collision vulnerability in rangeproof verification. An attacker minted ~3,998.5 L-BTC with no corresponding peg-in. Within minutes, the unbacked L-BTC was pegged out into real BTC on the Bitcoin mainnet. About 3,400 BTC was later returned to the federation peg wallet, while ~598.5 BTC remains under the attacker’s control. The SlowMist Security Team traced the fund flows on the #Bitcoin# side using @MistTrack_io and fully analyzed the incident. 🧩 Attack flow: 1️⃣ Two setup transactions first landed valid rangeproofs and commitments, while embedding a crafted payload in the locking script to seed node caches. 2️⃣ A follow-up minting output reused a colliding cache key — the same raw concatenation of proof, commitment, asset commitment, and scriptPubKey, but with different field boundaries. 3️⃣ On a cache hit, nodes skipped secp256k1_rangeproof_verify and min-value checks, accepted an unbacked commitment, and minted ~3,998.5 L-BTC. The fake UTXOs were consolidated and pegged out within minutes. ⚙️ Root Cause: The Elements rangeproof cache key concatenated variable-length fields without length prefixes. Distinct argument tuples could hash to the same key, so a positive cache hit meant skipping cryptographic verification. 🛡️ SlowMist Insight: A positive-result cache in a consensus verification path is itself a cryptographic primitive. Every field the verifier reads — and every field boundary — must be unambiguously bound into the key. Treat cache-key integrity as a mandatory item in consensus-layer audits. Full analysis👇

# Codex Features and Practical Usage 📝 Stop repeating the same context in every prompt. AGENTS.md is a layered instruction file that teaches Codex your build steps and code conventions for good. 🏷️ Title: Hierarchical Project Instructions (AGENTS.md) 🔗 URL: 📘 Overview AGENTS.md files are persistent instructions Codex reads before starting work. Encode your project norms, build commands, and code standards in Markdown, and Codex automatically discovers and applies them. The defining trait is a layered model where files closer to your current directory take precedence. ⚙️ How It Works Codex builds an instruction chain with strict precedence. ・Global (`~/.codex/`): checks `AGENTS.override.md` first, then `AGENTS.md` ・Project (Git root down to cwd): at each level checks `AGENTS.override.md`, then `AGENTS.md`, then custom fallback names ・Merge: files concatenate from the root downward with blank lines; later files (closer to cwd) override earlier ones It stops adding files once the combined size hits `project_doc_max_bytes` (32 KiB default). Naming conventions: ・`AGENTS.md`: the standard instruction file ・`AGENTS.override.md`: a temporary replacement that takes precedence at its level ・fallback names: configurable via `project_doc_fallback_filenames` (e.g. `TEAM_GUIDE.md`) 🛠️ Practical Usage Split by role for best effect. ・`~/.codex/AGENTS.md` (global): universal personal preferences, e.g. "Always run `npm test` after editing JS," "Prefer `pnpm`" ・repo-root `AGENTS.md`: team norms like linting standards, documentation expectations, and review criteria ・subdirectory `AGENTS.override.md` (e.g. `services/payments/`): override broader rules for a specific domain without deleting parent guidance Verify what loaded by asking Codex to summarize the current instructions, e.g. `codex --ask-for-approval never "Summarize the current instructions."` 💡 Use Cases In a monorepo where only the payments service needs a different test command or review bar, dropping a `services/payments/AGENTS.override.md` switches the rules on only when Codex works under that path. The "Review guidelines" you write here also apply to GitHub's `@/codex review`. ⚠️ Caveats Codex rebuilds the instruction chain on every run, so there is no cache to clear — if guidance looks stale, restart in the target directory. Empty files are skipped and non-existent fallback names are ignored. The `CODEX_HOME` environment variable overrides the default profile location. #OpenAICodex# #AGENTSmd#

# Useful but Little-Known Features of Claude Agent SDK 🌍 Staring at a blank screen while the agent works? Real-time streaming fixes that! Enable `include_partial_messages` to receive text and tool call chunks as they're generated. 📌 Title: Real-Time Partial Messages 🔗 URL: 🧩 Overview Setting `include_partial_messages` (Python) / `includePartialMessages` (TypeScript) to `true` causes the SDK to emit `StreamEvent` messages alongside regular `AssistantMessage` objects. `StreamEvent` contains raw Claude API streaming events — `text_delta` for text chunks and `input_json_delta` for tool input fragments. This enables responsive UIs with typing-style text display and tool execution status indicators. 🛠 How to Use ```python # Python - Display text chunks in real time from claude_agent_sdk import query, ClaudeAgentOptions from claude_agent_sdk.types import StreamEvent options = ClaudeAgentOptions(include_partial_messages=True) async for message in query(prompt="Explain how databases work", options=options): if isinstance(message, StreamEvent): event = message.event if event.get("type") == "content_block_delta": delta = event.get("delta", {}) if delta.get("type") == "text_delta": print(delta.get("text", ""), end="", flush=True) ``` ```typescript // TypeScript - Stream tool calls with status indicators import { query } from "@anthropic-ai/claude-agent-sdk"; let currentTool: string | null = null; for await (const message of query({ prompt: "Read the README.md file", options: { includePartialMessages: true } })) { if (message.type === "stream_event") { const event = message.event; if (event.type === "content_block_start" && event.content_block.type === "tool_use") { currentTool = event.content_block.name; process.stdout.write(`\n[Using ${currentTool}...]`); } else if (event.type === "content_block_delta" && === "text_delta") { process.stdout.write( } else if (event.type === "content_block_stop" && currentTool) { console.log(" done"); currentTool = null; } } } ``` 🏗 Integration into Production Systems - Append `text_delta` chunks to message bubbles in chat UIs for typing-style rendering - Detect tool names from `content_block_start` (type: `tool_use`) events and show "running..." spinners - Track message boundaries with `message_start`/`message_stop` events - Text deltas must be accumulated manually — prepare an accumulator buffer 💡 Use Cases 💬 Implement ChatGPT-style real-time typing display in custom UIs 🔧 Show the currently active tool name and progress in a status bar 📝 Start displaying long analysis results before the full response is complete ⚠️ Caveats - `StreamEvent` events are raw Claude API events, not accumulated text — you must concatenate text deltas yourself - Structured output (`structured_output`) is not sent as streaming deltas; it only appears in the final `ResultMessage` - UI state management is needed — e.g., pause text streaming while a tool is executing (track an `in_tool` flag) ✨ No more "Processing, please wait..." screens. Partial messages bring users closer to the agent's work in real time! #ClaudeAgentSDK# #AIAgent#

Fable 5.1 Masked a green screen on multiple shots And it did it around 90% quality of a human! Instruct your Claude: 1. Prompt a flat, unobstructed green screen. 2. Get word timings for slide cues. 3. Detect green per frame (HSV threshold). 4. Restrict detection near previous quad. 5. Fit four-corner quad to blob. 6. Smooth quads over five frames. 7. Mask only green inside quad. 8. Never key green outside quad. 9. Foreground objects preserved by mask. 10. Cover-fit slide, crop evenly. 11. Perspective-warp slide into quad. 12. Hard-cut slides at cue timestamps. 13. Despill: clamp G to max(R,B). 14. No add-back, avoids magenta. 15. Green-hued pixels get full alpha. 16. Raise thresholds where hands cross. 17. Static shot: freeze reference quad. 18. Fast motion: unsmoothed per-frame fit. 19. Video slide: warp every frame. 20. Grade slide: darker, slight blur. 21. Re-render only changed clips. 22. Normalize clips before stitching. 23. Stitch with concat demuxer only. 24. Verify audio per segment. 25. Check frames with your eyes. If you still think AGI won't happen this year I invite you to think again After effects people Your days are numbered