Tim Adelmann

"Claude Code just SAVED my marriage!" I gave Claude Code 512 GB of HEAVILY corrupted wedding footage from a close friend of mine, and within 10 minutes, recovered it all. Two days ago, I got some bad news from a buddy telling me how the only copy of their wedding videos was corrupted to do a severe fault in the memory card. Just like that, someone's happiest day turns into a nightmare. The footage was practically gone, nothing was able to read the memory card, and the only option was to pay thousands of $$$ or let it go. Until I had an idea... What if I plugged in the memory card and let Claude Code have a go at it. It first made a copy of the corrupted files onto my computer using PhotoRec as to keep the original drive untouched. For context, I have never done anything like this before, it did this itself. It looked at memory slices from the IDR, then began using FFMPEG to reconstruct the SPS, IDR, P/B, etc. all without me touching it once. I come back to the full 512GB of uncorrupted video I can send back to my good friend. The power of Claude Code is insane.

Nvidia paid 3X Groq's September valuation to acquire it. This is strategically nuclear. Every AI lab was GPU dependent, creating massive concentration risk. Google broke free with TPUs for internal use, proving the "Nvidia or nothing" narrative was false. This didn't just demonstrate technical feasibility, it revealed that Nvidia's moat was shallower than markets believed. When a hyperscaler successfully builds custom silicon, every sophisticated buyer starts running" should we build our own?" calculations. This drops Nvidia’s TAM. Jonathan Ross (Groq’s founder) is the inventor of TPU. He understood the architectural principles that made non-GPU AI acceleration viable. His LPU architecture targeted inference workload where GPUs are actually over-engineered. This matters because inference is where the real money is long-term. Training is one-time capex, but inference is recurring opex that scales with usage. If Groq proved LPUs could hit competitive price-performance on inference, every cloud provider would white-label their architecture. Nvidia would get squeezed into "just training" while losing the annuity stream. It is safe to see this deal as Nvidia insuring against Groq enabling an entire ecosystem of Nvidia alternatives. But what is more interesting is the second-order effect, the customer lock-ins. Now, Nvidia owns both the incumbent standard (CUDA + GPU) and the most credible alternative architecture (LPUs). This is MSFT buying Github-level strategic. Any AI lab evaluating "build vs buy vs alternative vendor" now faces: - Option A (Nvidia GPUs) - Option B (Nvidia <> Groq LPUs) - Option C (start from scratch) Turning a competitive threat into a customer segmentation tool, Jensen is the master of trades. They can now price-discriminate: premium customers pay for GPUs, price-sensitive inference gets funneled to LPUs, and Nvidia captures both. If Nvidia doesn't integrate LPUs in its roadmap, this was a pure defensive play. If they do integrate it and start offering "GPU for training, LPU for inference" bundles, this becomes a textbook moat-widening acquisition. The most expensive thing in technology isn't building the future, it's preventing someone else from building a future without you.

6 months ago to now