Two years ago, we embarked on a journey to democratize and preserve human intellect through our DigitalMe project. While AI technology has evolved rapidly since then, our vision remains as relevant as ever. This throwback showcases our early implementation using D-ID, ElevenLabs,

From literature scout to autonomous lab partner: Large‑language models are racing up the R&D value chain. 💡. This survey maps three maturity tiers, Evaluator → Collaborator → Scientist, and shows that workflow design, not just model size, is what unlocks 70% faster

A monumental leap forward for humanoid robotics! 🤖🔋 . Chinese company UBTech has unveiled their next-gen humanoid robot, Walker S2, and it boasts a world-first capability: it can swap its own battery!. Why we’re watching at RediMinds ⤵️. 1️⃣ Hospital logistics – imagine robots.

Google DeepMind’s Mixture‑of‑Recursions (MoR) just rewrote the efficiency playbook:. 🔄 Token‑level routing picks the right layers on the fly.📉 ⌀ 50% smaller KV‑cache & 2× faster inference at equal quality.🏥 Practical win for on‑prem clinical copilots & edge‑deployed

LeCun’s roadmap is a sharp reminder that today’s text‑only generative stacks are at best proxies for grounded intelligence. Energy‑based objectives + joint embeddings would let representations be scored (or rejected) against physics and perception rather than next‑token entropy;.

🧠 Reasoning engines, not just larger models. BRiTE—Bootstrapping Reinforced Thinking—shows how an LLM can teach itself better chains‑of‑thought by alternating two simple moves:.1️⃣ Generate many rationales ➜ score them with an external verifier.2️⃣ Distill the top‐scoring

🌀 "The word 'observe' in physics isn't psychological - it's measurement." - Neil deGrasse Tyson. Just like quantum particles maintain their mysterious connection across vast distances, our teams at RediMinds operate with a kind of "quantum collaboration" - where insights,

🧬 BioEmu 1.1 → from GPU-years to GPU-hours. Microsoft Research just open-sourced a generative engine that emulates the full motion landscape of a protein in < 60 minutes—sampling conformations, cryptic pockets, and ΔΔG shifts with < 1 kcal/mol error. 🔍 Capability → 🚀 Impact

Grok 4’s token pricing sits between Gemini 2.5 Pro (cheaper) and o3 (newly cheaper on output), and its speed trails both. For one-off analytical tasks the extra IQ may justify the spend, but in high-volume workflows throughput often wins. The real contest now is who can pair.

Google Research just dropped two powerful, open-weight health models — and they both run on a single GPU. 🔹 MedGemma 27B Multimodal → complex image + EHR reasoning.🔹 MedSigLIP → ultra-light image-&-text encoder for triage, search & retrieval. With SOTA scores on MedQA and

Meet Reachy Mini—an open-source desktop robot built for coding, learning, and rapid AI prototyping at just $299-$449. 🤖💡. Created by Hugging Face + Pollen Robotics, it ships with plug-and-play vision, speech and action models, so your LLM agents can leave the screen and grab.

Love seeing transmission design catch up with the safety-first requirements of healthcare robots. A drive that back-drives gracefully and costs 50% less could unlock sterile-room assistants far sooner than expected. Watching closely as these units move from lab rigs to volume.

Great addition to the open-source agent stack. For regulated domains we’d love to see a pluggable guardrail layer (policy checks, SBOM tagging, HIPAA/GDPR linting) that inserts before commits. Pair that with Anthropic’s safe-complete and you’ve got a pathway from weekend hack to.

This is it. The moment AI moves beyond the screen and into the clinic, directly impacting human health. 🚀🔬. Google DeepMind's Isomorphic Labs, born from the revolutionary AlphaFold breakthrough, is gearing up for its first human trials of AI-designed drugs with the grand

Love the open-source approach with OpenDia, but remember: once the model can press your buttons it can also press the wrong ones. Role-based scopes, click-limits, and transparent logs will be key to keeping auto-web safe for production use.

Hunyuan’s BPT compression + autoregressive mesh tokens = big leap: higher detail without exploding face count, and tri/quad toggles mean no painful re-meshing downstream. Excited to see how it slashes iteration cycles for UE & Unity teams.

A 60 M-parameter encoder-decoder that self-calibrates to new clusters with 500 samples is huge for edge environments. Swap costly simulators for a compact T2T model and you free up both GPU hours and power budget; the kind of efficiency win ops teams have been waiting for.

Hunyuan-A13B’s MoE footprint is small enough for hospital data centers yet strong on clinical-reasoning benchmarks. We’re lining it up for internal evaluation as a potential backbone for multimodal radiology agents; if its latency/accuracy balance matches the paper, it could make.

MCP is basically the USB spec for cognitive workloads, one plug to expose any data-oriented peripheral (calendar, email, DB) to whatever reasoning engine you prefer. Once the interface is standardized, capability scaling becomes a matter of swapping hosts, not rewriting glue.

Unlocking collective intelligence for safer, smarter Med-AI. AB-MCTS shows how multiple frontier LLMs can cooperate, beat solo models on ARC-AGI-2, and hint at an ensemble future for high-stakes domains like diagnostics and drug design.

Copilot on the Pitch: How Microsoft & the EPL Are Personalizing Football for 4.7B Fans.