Creator of Sqlite on pull requests: "You say, oh, it's free. No. It's not free. What you're doing is asking me ... to maintain it for you, to to document it for you, to test it for you, to maintain it for you for the next 25 years. That's not free." Yep. Wise words from a wiser man than me. I've told people for the past decade and I have recent posts on here saying the same: the merge button is the easy part. Its the decade+ (Richard says 25 years) that follows where you've accepted the transfer of maintenance thats hard.

Import the Wispr Flow dictionary into Codex! Wispr Flow stores its learned dictionary in a local SQLite database on macOS: ```text ~/Library/Application Support/Wispr Flow/flow.sqlite ``` Just tell codex to make sure to merge them with the existing `dictationDictionary` field in config.toml

Need some data points from ya'll. How many prompts do you actually type per day vs. how many your agents send themselves? Paste into Claude Code: "Index ~/.claude/projects/**/*.jsonl into SQLite. Count user records where origin.kind == "human" — ignore promptSource, sdk ≠ robot — grouped by local date. Give me mean/day."

We gave Grok Build 0.1 one prompt: build a webhook delivery service in TypeScript, Bun, and SQLite. It planned it, built it, and shipped a working demo. Total cost: $1.65. Zero tool-calling failures. Here's exactly what happened, file by file.

# Hermes Agent Features and Practical Usage 🚀 Pick up exactly where you left off with a single command. Hermes Agent Sessions automatically record, resume, and search every conversation, forming the backbone of long-running agent operation. 📌 Title and Feature URL Title: Sessions URL: 📝 Overview Sessions automatically save every interaction, whether from CLI, Telegram, Discord, Slack, or other platforms. Full message history is persisted to SQLite, so you can resume past work or surface old exchanges via full-text search. Because sessions are tracked per platform, each chat naturally keeps its own context. 🔧 How It Works - History lives in `~/.hermes/state.db` (SQLite, WAL mode), tracking metadata, full message history, token counts, and an FTS5 full-text search index. - Only the current conversation window loads into active context, not every historical byte. Images become descriptions, audio is transcribed, and documents are summarized rather than re-sent. - After the first exchange, a background auxiliary model auto-generates a descriptive 3-7 word title with no added latency. - Sessions are keyed deterministically by source, with distinct formats for DMs, groups, and threads. 🛠 Practical Usage - Resume the latest CLI session: `hermes --continue` (or `-c`); resume by title with `hermes -c "project name"`, or by ID with `hermes --resume `. - List/export/manage: `hermes sessions list --limit 50 --source telegram`, `hermes sessions export backup.jsonl`, `hermes sessions prune --older-than 90 --yes`, `hermes sessions stats`. - Manual naming: `/title my project` in chat, or `hermes sessions rename "new title"`. - The agent itself uses the `session_search` tool (FTS5) and auto-references past chats when you say things like "remember when." - Hand off an active CLI conversation to a messaging platform with `/handoff telegram`, preserving the full transcript. 🎯 Use Cases - Resume yesterday's refactor with all prior context intact. - Quickly recall "what did we decide back then" via full-text search. - Keep separate context per entry point (Telegram, Discord) to avoid crosstalk. - Use it as a work-history database for long-running agents. ⚠️ Caveats - Auto-titling runs only once per session and skips if a title already exists. - Media bytes are never re-sent; only derived text or file paths persist in later context. - Auto-pruning (` is disabled by default; active sessions are never pruned regardless of age. - On non-thread platforms with shared group home channels, genuinely shared group chats aren't handled ideally. #HermesAgent# #AIAgents#

this tool turns any API into a working CLI for your agents... APIs don't come with an interface agents can just pick up... CLIs are the format you want for agentic workflows i'm using PrintingPress ALL THE TIME, this is how it works: > point it at an API or a website with no public API and it sniffs the endpoints itself > it reads the official docs plus every community CLI and MCP server for that service, then prints a token-efficient Go CLI: agent-native flags, compound commands, a local SQLite mirror > every print ships with a matching Claude Code skill and an MCP server, and printingpress․dev holds a catalog of already-printed CLIs you can install directly when you run on as many external tools as i do, the context it hands back to your agents is the whole point (this isn't sponsored & tool is free) ty @mvanhorn for this beauty

🚨 SlowMist TI Alert 🚨 MistEye has detected a large-scale npm supply chain compromise impacting the Keyv/Cacheable ecosystem. Attackers published over 2,000 malicious package versions across the affected ecosystem, including keyv@6.0.0. Keyv, a widely used key-value storage abstraction with adapters for Redis, SQLite, PostgreSQL, MongoDB, and other backends, has roughly 127 million weekly downloads, leading to significant downstream supply chain exposure. The attackers' tradecraft closely mirrors techniques previously observed in the Shai-Hulud npm worm campaign, pointing to a highly automated and scalable supply chain attack. Potential attacker actions include credential theft, environment variable exfiltration, CI/CD secret compromise, remote payload delivery, and lateral propagation through compromised development environments. Security teams should immediately identify and remove affected package versions, upgrade to verified safe releases, review dependency lockfiles and build logs, monitor for suspicious outbound connections, rotate exposed credentials, and rebuild impacted environments from trusted sources if compromise is suspected. You can also visit to check for free whether the npm packages, pip packages, domains, or IPs you use are safe. As always, stay vigilant!

🚀 SlowMist has officially released MistEye DNS Guard, a lightweight local DNS relay and threat observation tool built with Rust for macOS and Linux. By turning DNS into an observation point, MistEye DNS Guard helps detect malicious domains, public IPs, and outbound process connections — while keeping normal DNS resolution unaffected through asynchronous threat detection. 🌟 Key capabilities: 🔹DNS relay & system DNS takeover 🔹Domain and public IP threat detection 🔹Outbound process monitoring 🔹Malicious event retention & Webhook alerts 🔹Lightweight deployment with built-in SQLite persistence, no external database required ⚡ No complex infrastructure required — download the precompiled binary, configure the TOML file, and start monitoring. 📄 Learn more about MistEye DNS Guard: 🛠️ Explore the open-source project:

Burning tokens on AI code review? 🔥 This tool turns your codebase into a structural graph so the AI reads only relevant files — cutting tokens by a median of 82x. Title: tirth8205/code-review-graph URL: 🔥 Overview A local-first code intelligence tool that persists a structural map (graph) of your codebase via Tree-sitter parsing. It lets AI assistants review by reading only contextually relevant files instead of the whole repository. ❓ Challenges Solved AI code review tools waste tokens by re-reading large parts of the codebase on every review. ・In large monorepos, context bloats and both cost and latency worsen ・Analyzing change impact required scanning the entire project 💡 Methodology & How It Works A three-stage pipeline. ・Parsing: Tree-sitter builds ASTs, extracting functions, classes, imports, and call relationships ・Graph storage: nodes and edges persist in SQLite (no external database) ・Analysis: on changes, blast-radius analysis traces affected callers, dependents, and tests, returning minimal context It supports many languages, incremental updates under 2 seconds, MCP integration (30 tools), a GitHub Action, and D3.js visualization. 📊 Experimental Results ・Token efficiency: 38x-528x reduction (median ~82x across 6 repos) ・Impact prediction F1 score: 0.71 average ・CLI example: full context 12,921 tokens → graph context 762 tokens (~94% saved) #CodeReview# #AIAgents#