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Show HN: Beehive – Multi-Workspace Agent Orchestrator https://ift.tt/wJD3ryv

Show HN: Beehive – Multi-Workspace Agent Orchestrator hey hn, i built beehive for myself mostly. it has gotten to the point where my work consists in supervising oc or cc labor at tasks for multiple issues in parallel. my set up used to be zellij with a couple tabs, each tab working in a separate dir and it was a pain to manage all that. i know i could use git worktrees but they're kind of complicated, if you don't know how to use them it is easy to mess up, and i just prefer letting agents run in separate dirs with their own .git and not risk it. while i like zellij and use it inside beehive, i dont like the tabs and i forget where i am half the time. beehive is a way for me to abstract that away. the heuristic is simple - hives are repos, so you basically have a bunch of hives which correspond to repos you work out of. each hive can have many combs. a comb is a dir with the copy of the repo you're working on. fully isolated, standalone, no shared .git. so for work or for ...
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Show HN: Gonzales – Self-hosted internet speed monitor with Home Assistant https://ift.tt/dnpoFg8

Show HN: Gonzales – Self-hosted internet speed monitor with Home Assistant https://ift.tt/5H8m17G February 26, 2026 at 10:56PM
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Show HN: Live iOS 26.3 exploit detection (CVE-2026-20700) – Multi-region C2 https://ift.tt/OrSAehv

Show HN: Live iOS 26.3 exploit detection (CVE-2026-20700) – Multi-region C2 Public release of *ZombieHunter*, a forensics tool detecting live exploitation of CVE‑2026‑20700 (dyld memory corruption) in iOS 26.3. Analysis of sysdiagnose archives shows identical exploit shells showing different C2 endpoints: US Device 1 → 83.116.114.97 (EU/US) US Device 2 → 101.99.111.110 (CN) The rogue dyld_shared_cache slice triggers overflow via malformed `mappings_count`, executes shellcode (BL #0x15cd), and applies an AMFI bypass (`DYLD_AMFI_FAKE`) enabling unsigned code persistence. Apple PSIRT + CISA were notified; public disclosure follows. Sample: https://drive.google.com/file/d/1rYNGtKBMb34FQT4zLExI51sdAYR... SHA256 artifact: ac746508938646c0cfae3f1d33f15bae718efbc7f0972426c41555e02e6f9770 Usage: `python3 zombie_auditor.py sysdiagnose_xxx.tar.gz` (Needs capstone) Reproducible PoC confirms CVE‑2026‑20700 bypass, AMFI neutralization, and live C2 connectivity in production iOS 26.3. https://ift.tt/...
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Show HN: MasqueradeORM – Memory Efficient Node ORM: Just Write Classes https://ift.tt/urTfaw8

Show HN: MasqueradeORM – Memory Efficient Node ORM: Just Write Classes https://ift.tt/sZQDnf8 February 25, 2026 at 12:41AM
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Show HN: Ghist – Task management that lives in your repo https://ift.tt/EBM6xhp

Show HN: Ghist – Task management that lives in your repo https://ift.tt/nvkyBEx February 24, 2026 at 11:55PM
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Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) https://ift.tt/CUvzc4W

Show HN: AgentDbg - local-first debugger for AI agents (timeline, loops, etc.) AgentDbg is a local-first debugger for AI agents. It records structured runs (LLM calls, tool calls, state, errors) to JSONL and shows the timeline UI locally. There is no need for cloud, accounts, and no telemetry. Flow is as simple as: 1. Run an agent 2. `agentdbg view` 3. Inspect the timeline, loop warnings, errors, etc. v0.1 includes `@trace` and `traced_run`, recorders, loop detection, best-effort redaction (by default), local UI, export. I also started working on integrations: there is an optional LangChain/LangGraph callback. * Repo: https://ift.tt/glkfxKp * Demo: `python examples/demo/pure_python` and then `agentdbg view` Would love feedback on: 1. Trace format 2. Integrations to prioritize in the next several days 3. What you would want for deterministic replay https://ift.tt/glkfxKp February 24, 2026 at 12:44AM
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Show HN: PgDog – Scale Postgres without changing the app https://ift.tt/LCiURh2

Show HN: PgDog – Scale Postgres without changing the app Hey HN! Lev and Justin here, authors of PgDog ( https://pgdog.dev/ ), a connection pooler, load balancer and database sharder for PostgreSQL. If you build apps with a lot of traffic, you know the first thing to break is the database. We are solving this with a network proxy that works without requiring application code changes or database migrations. Our post from last year: https://ift.tt/3KV5qCE The most important update: we are in production. Sharding is used a lot, with direct-to-shard queries (one shard per query) working pretty much all the time. Cross-shard (or multi-database) queries are still a work in progress, but we are making headway. Aggregate functions like count(), min(), max(), avg(), stddev() and variance() are working, without refactoring the app. PgDog calculates the aggregate in-transit, while transparently rewriting queries to fetch any missing info. For example, multi-database average calculation requires a...
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