The Problem AI agents are moving from answering questions to taking actions — calling APIs, querying databases, executing code, managing memory. The security surface has shifted from "what the model says" to "what the agent does." Most guardrail solutions address the first problem. They filter content. They detect prompt injection. They moderate output. These are necessary but insufficient. The
Mistral Large 3 launched in December 2025 as Mistral's flagship open-weight model. Six months later it remains the largest model Mistral has publicly released under a permissive license. This guide covers the architecture, benchmarks, pricing, and practical considerations for developers deciding whether to use it in 2026. Mistral Large 3 (model ID mistral-large-2512, the 2512 indicating December 2
What is Mycelium? (2 para) The problem we're solving (2 para) Discovery benchmark Dataset (1k agents, 1k queries) Results table Keyword vs Semantic graph (ASCII) Load benchmark Cache architecture Results table What changed (before/after cache) How to reproduce pip install code snippet What's next (roadmap) GitHub link -> / mycelium 🍄 Mycelium Agents Watch 3 AI agents c
A few months ago I started with a simple goal: have a solid, reusable base for my PHP projects without pulling in a full framework every time. What I ended up with is something I'm genuinely proud of, and today I'm making it public. php-template is a PHP 8.2 MVC starter template with serious tooling, full testing stack, and something I haven't seen in other PHP templates: native support for AI age
Metric Value Django Average Response Time 287ms Node.js Average Response Time 193ms Django Memory Usage (1000 users) 1.8GB We tested Django 4.2 and Node.js 18.16 under identical conditions to measure their performance for reporting dashboard workloads. The test environment consisted of AWS EC2 m5.2xlarge instances (8 vCPUs, 32GB RAM) running Ubuntu 22.04. Both frameworks connected to th
I was reading an Anthropic engineering post this winter that mentioned, almost in passing, that Claude Code's biggest token sink across their fleet is package-related queries. Every "how do I do X in Y", every npm install, every dependency audit. The model fetches the registry JSON, reads it, summarizes for itself, and only THEN answers you. I started measuring it on my own agent traffic. 74% of t
If you've ever built ETL pipelines pulling data from MongoDB into Delta Lake using Spark, you've probably hit this wall. The pipeline works fine — until it doesn't. A single document with an unexpected shape is enough to break the entire write, leave the table in an inconsistent state, and send your on-call engineer digging through Spark logs at 11pm. I built and maintained more than 10 of these j
By Nasarah Dashe This is Challenge #2 in a series. Read Challenge #1 here. Imagine waking up to 50 missed calls from your bank. You check your account balance. It is empty. A SIM‑swap fraudster convinced your telco agent to transfer your number to another SIM card, then used it to reset your mobile banking PIN and drain every kobo. Later that week, you receive an email from "Flutterwave Support" a