I find it super funny that most open source development is happening on Microsoft infrastructure (github). The Universe surely must be having fun with us :) (And kudos to Microsoft for coming around and realising we were right all along:).

Achievement unlocked: I got scammed in Austria. Used to be high trust society. Now just enjoying the decline... Thankfully only 23€

This is so cool! View article →

The cheapest per-token inference... If you've looked at @routstr and want cheap AI inference paid in sats (without e-mail address and without KYC) but don't want to manually pick and babysit nodes, run routstrd locally. First the prices. I am monitoring prices and for most models, you can get somewhere between 53% and 66% of @PayPerQ (also pay per query) price on Routstr. Yep, between half and two-thirds. Some models, such as Kimi K3 are often 36%. And PPQ is around 5% more expensive than OpenRouter. I do not know of a cheaper way to get per-token API inference right now (besides subscriptions, they are a different beast). Back to routstrd. It's a small daemon, not a node itself, that sits on your machine and exposes a normal OpenAI-compatible API on localhost:8008. Point your apps at it and it handles the rest. Under the hood it finds nodes by reading provider announcements off Nostr relays, verifying them and refreshing the list every 21 minutes, and it only trusts nodes that have a positive review from the Routstr team — anything unreviewed is disabled by default. For whatever model you're calling, it compares prices across all known nodes and routes to the cheapest one, and if a request fails it automatically fails over to the next cheapest instead of just erroring out. On payments, it defaults to a mode where it quietly exchanges a Cashu token for each node's own API key, holds onto it, and tops it up as the balance runs low — so it genuinely manages real provider API keys for you behind the scenes. You fund it with Lightning or a Cashu token, and your apps never touch the e-cash directly, they just get a local key from the daemon. Worth trying, just keep the wallet funded with small amounts you're comfortable topping up regularly rather than loading it with a lot of sats at once. Basic commands to get going: bun i -g routstrd routstrd onboard routstrd receive <cashu-token> # fund with a Cashu token routstrd receive 2100 # or top up 2100 sats via Lightning routstrd start # starts the daemon on localhost:8008 routstrd clients add --claude-code # or --pi-agent / --opencode routstrd status routstrd balance P.S.: I run price monitoring of Routstr nodes and I only count nodes that respond to API requests with completion - so these prices are representative of real prices that you can get, not only advertised prices.

AI corporations borrowing like there's no tomorrow and subsidizing tokens (although I'm not really sure if it's a true subsidy or the API is just overpriced). It can pay off for them, but they need to lock you in. And they're trying. Anthropic wants you to use their harness (Claude Code) - and it is really good. OpenAI now launching Dots. The idea is vendor lock in. I don't have a grand scheme for how this is going to end, but my personal strategy - never lock in. Use OpenCode, hermes-agent, be as model promiscuous as you can. Use the best frontier models, use the cheap flash models, milk most value for the buck, but change providers often and never accept lock in. They think they're building network effects, but you make them burn cash and provide insane value to you. Be the winner.

Can a local model replace a cloud Jev as a prompt-injection gate for my AI agent? I benchmarked the new decision models in Ollama 0.35 on 718 test items. Nimble 9B came closest: it caught 81% of attacks (Jev on Venice: 89%) and it's the best local model I've tested. But it blocks only 63% of instructions planted in ordinary emails, against Jev's 92%. I'm keeping Jev for now. If you want nothing to leave your machine, Nimble is now one config line in the hermes-firewall plugin (~11 GB RAM).

A prompt-injection gate for my AI agent: what worked, what didn&#8217;t, and the benchmark &#8211; Juraj Bednar

A prompt-injection gate for my AI agent: what worked, what didn’t, and the benchmark

A prompt-injection gate for my AI agent: what worked, what didn&#8217;t, and the benchmark &#8211; Juraj Bednar

Repo:

GitHub

GitHub - jooray/hermes-firewall: Prompt-injection gate for Hermes Agent: scans web, MCP, email and image content before the model sees it. Plugin + benchmark.

Prompt-injection gate for Hermes Agent: scans web, MCP, email and image content before the model sees it. Plugin + benchmark. - jooray/hermes-firewall

OK, this is beautiful: "I built a patcher (tools/patch_libapp.py) that edits libapp so inside the installed split APK at identical size — no reinstall, no re-signing, no re-login — and rewrites both zip CRCs so the archive still verifies. Deploying is a single adb push. I confirmed each patch is live by reading /proc/<pid>/mem."

Running opencode 2. So far, good.

Everything is now "source-available". No proprietary algorithms exist. "Grab the apk from my phone in USB debug mode connected to this computer using adb. Decompile, analyze how XXX works, give me auditt. Prepare an independent implementation." DeepSeek V4.1 Flash does this extremely cheap. Then you can verify with more expensive model if something is not 100% right. It does not have to be bytecode, I see a bunch of assembler in the outputs, it's just reading the functions from native libraries.