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Microsoft just figured out how to store data for 10,000 years… in glass. Claude just got 90% cheaper for agents. And a tiny web agent quietly beat Gemini, OpenAI, and Anthropic…!?
What's on FIRE 🔥
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AI INSIGHTS
A diagram depicting the setup for writing phase voxels into the glass media
Hard drives fail. Tape degrades. Archives don’t last.
Microsoft Research just published in Nature: Project Silica can store data in glass for 10,000 years. And it now works on ordinary borosilicate glass (like kitchen cookware), not just expensive fused silica.
What changed:
Old → Many laser pulses | New → Single-pulse “phase voxel”
Old → 3–4 cameras | New → 1 camera
They also enabled faster parallel writing and used machine learning to reduce errors.
This isn’t theory. Project Silica already stored Superman (Warner Bros.), partnered with Global Music Vault, and built “Golden Record 2.0.”
AI impact:
Modern AI depends on huge datasets and long-term records. If storage degrades, future retraining and compliance break. Glass storage offers durable, tamper-resistant archives for datasets and model checkpoints.
Why it matters: Archives think in centuries. Today’s storage lasts decades. If Azure turns this commercial, long-term storage may move from tape… to glass slabs built to outlive us.
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AI SOURCES FROM AI FIRE
1. AI Career Tsunami Playbook: The exact 3-step shift to survive 2026. Stop being the “Script worker.” Use the RAIL test, automate boring tasks, and move to Strategy before 1.1M+ job losses becomes your story.
2. $1 AI Business Blueprint: Launch a real store in 60 minutes. No code. No team. Copy this dropshipping + AI automation stack (Shopify $1 deal + AutoDS + AI copywriting) and go live today.
3. 3 Passive Income Machines with OpenClaw. Forget basic chatbots. Build autonomous agents that run 24/7 - from VA agency bots to arbitrage scanners and automated content sites.
TODAY IN AI
AI HIGHLIGHTS
🧊 At India’s AI Summit, refused went viral. Altman and Amodei awkwardly avoided Modi’s hand-link moment - after weeks of OpenAI vs Anthropic tension.
⚡ Claude API now has auto prompt caching. Cached tokens cost 90% less, and one cache_control handles it. For agents, this changes the economics.
📸 Google Pomelli added Photoshoot. Turn basic product pics into studio-style marketing shots using Business DNA + Nano Banana.
🏆 Web agent TinyFish hit 90% on Mind2Web, beating Gemini, OpenAI, and Anthropic. All 300 runs are public.
📊 Anthropic launched Claude in PowerPoint. It builds and edits slides while keeping templates and enterprise security intact.
💰 AI Fundraising: Adronite raised $5M Series A led by Gatemore Capital Management to scale its AI codebase intelligence platform. The 2023 startup analyzes entire software systems across 20+ languages and prepares for commercial deployments in Q1 2026.
HOT PAPERS OF THE WEEK
Secure Sandboxing for Local Coding Agents
Cursor ships cross-platform sandboxing on macOS, Linux, and Windows. Sandboxed agents stop 40% less often, cutting approval fatigue while protecting files, git, and secrets. (Cursor)GLM-5: From Vibe Coding to Agentic Engineering
GLM-5 upgrades coding models with asynchronous RL and lower-cost scaling, pushing state-of-the-art on real end-to-end software tasks. Code and models are open. (Z.ai)EVMbench: Benchmarking AI for Smart Contract Security
OpenAI and Paradigm launch EVMbench to test detect, patch, and exploit skills. GPT-5.3-Codex scores 72.2% in exploit mode, showing fast gains in AI cyber capability. (OpenAI • Paradigm)
NEW EMPOWERED AI TOOLS
🧠 Gemini 3.1 Pro is a smarter reasoning model, built for complex tasks and deeper problem-solving.
📸 Google Pomelli turns one product photo into studio-quality images, ready for marketing in minutes.
🧩 Architect by Lyzr builds multi-agent AI systems visually, with full control and zero black boxes.
🔗 Claudebin lets you export Claude Code sessions as shareable URLs, complete with files, commands & context.
AI BREAKTHROUGH
For years, we trained AI to chase rewards. Get a score. Improve it.
But humans learn differently. We try, fail, reflect, then retry.
A new paper from Microsoft + University of Southern California + University of Pennsylvania introduces Experiential Reinforcement Learning (ERL).
Here’s how it works:
1️⃣ First attempt
2️⃣ Written self-reflection from feedback
3️⃣ Second, improved attempt
4️⃣ Reinforce the better sequence
Instead of just seeing one final reward number (like in standard RLVR), the model sees why it failed.
On Qwen3-4B:
• Sokoban: 0.06 → 0.87
• HotpotQA F1: 0.45 → 0.56
The improved attempts are distilled back into the base model for deployment.
Small tweak. Big shift.
AI is shifting from “maximize reward” → to “learn from mistakes.”
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