Claude's biggest weakness in coding just got fixed. EverOS built the solution on a memory layer you can actually read, edit, and inspect. here's what it does: every agent today wakes up with amnesia. Claude Code, Codex, OpenClaw, Hermes — they all start from zero every session. your decisions, workflows, and lessons don't follow you. EverOS is an open-source memory layer that fixes that. and it's a full developer-focused rebuild — easy to run locally, easy to inspect, easy to integrate. the six things it ships in 1.0.0: → Markdown as source of truth: memory stored as readable .md files you can grep, version with Git, and open in Obsidian → local stack: Markdown + SQLite + LanceDB. no MongoDB, Elasticsearch, or Redis needed → dual-track memory: agent memory (skills) and user memory (profile) extracted separately → multimodal ingestion: text, images, audio, PDFs, HTML, email, all unified into searchable memory → self-evolution: repeated workflows become reusable skills without retraining → orthogonal retrieval: search independently by user, agent, app, project, or session but the bigger idea is where it's heading: → Knowledge Wiki: your conversations, files, and decisions become a living knowledge layer both humans and agents can read and reuse → Reflection as Dreaming: agents process past work offline, extract patterns, and wake up better prepared remember → reflect → dream → evolve. memory that's open, portable, and user-owned. it moves across your tools instead of being trapped in one. if you're building agents, coding assistants, or anything that should remember what happened before, this is one of the most practical open-source memory libraries for builders right now. repo is open. star it, run a use case, break it, open an issue 👇 #EverOS# #EverMindAI#

Do you understand what Google just did? They put a frontier AI model on your laptop. free. open source. it's called Gemma 4 12B. and it just dropped. here's what makes it wild: → runs locally with just 16GB of VRAM → vision, audio, and agentic reasoning built in → performance nearing Google's much larger models → no separate encoders: one unified architecture handles everything → apache 2.0 license: use it for anything, including commercial projects this isn't a stripped-down demo model. it's a full multimodal AI that sees, hears, reasons, and acts. on your laptop. without the cloud. without the cost. without sending your data anywhere. the architecture change is the most interesting part. most multimodal models bolt on separate vision and audio encoders. Gemma 4 12B removes them entirely. one unified model handles every modality from the ground up. the result: more capable. smaller footprint. faster locally.

Do you understand what Anthropic just did? they just filed to go public. the draft S-1 hit the SEC today. the most anticipated AI IPO is officially in motion. let's put this in perspective: → days ago they hit a $965B valuation → that's nearly triple their February number ($380B) → they already passed OpenAI in value and revenue → $47B annual revenue, up from $9B a year ago → now they're taking it public here's the part that makes it wild: 5 years ago Anthropic didn't exist. Dario Amodei left OpenAI in 2021. took his sister Daniela and a handful of researchers. no product. no revenue. just a bet on building AI safely. today that company is filing for one of the biggest tech IPOs in history.