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Have you ever tried keeping up with the AI industry? It's a massive wall of noise. SGNL Intelligence is fixing that using something completely fascinating called GIKE. We send out a literal swarm of AI agents to process every claim in tech, and then a human editor steps into the loop to verify the truth. We don't just summarize the news; we find the signal.
See how GIKE works under the hood →- Samsung Is Juggling Knives. One Might Drop.
Samsung is making NVIDIA's memory, fabricating NVIDIA's inference chips, developing next-gen HBM4E, and building two Texas fabs — all while its workforce moves toward a strike. One company, four critical roles, zero backup on the most important one.
- NVIDIA's Rubin Is Late. Here's Who Wins and Who Gets Squeezed.
NVIDIA's next-gen Vera Rubin GPU — promising 5x inference over Blackwell — is reportedly delayed one quarter because the world can't make HBM4 fast enough. Google TPUs rise, AMD gets more time to close the software gap, and AI labs scramble for compute. The memory wall just hit NVIDIA's roadmap.
- Connecting the Dots: Why AMD Is the Only Company That Doesn't Need an Acquisition for the SRAM Inference Revolution
The AI inference stack is splitting in two. NVIDIA bought Groq for SRAM. AWS rents Cerebras. But AMD already owns the deepest SRAM Compute-In-Memory IP in the industry through Xilinx — and they're the only company with GPU + FPGA/CIM + NPU + CPU under one roof. They just haven't connected the dots yet.
- Michael Burry vs NVIDIA: The Bear Case Hidden in the 10-K
Michael Burry's NVIDIA bear case has evolved from Twitter hot takes to forensic 10-K analysis. We trace his thesis through three layers: the shovel-seller narrative, the NVIDIA-OpenAI circular capital flow, and what the actual SEC filings reveal about $117B in supply commitments, permanently extending cash cycles, and hidden compensation costs. Then we stress-test it against NVIDIA's record-breaking fundamentals.
- The Machine That Writes the Machine: AI Kernels Surpass a Decade of Human Expertise
DoubleAI's WarpSpeed rewrote every kernel in NVIDIA's cuGraph library and beat all of them -- 3.6x average speedup with 100% correctness. General-purpose LLMs hit only 56-59%. Here's why this matters: AI hasn't just learned to code -- it's learned to write the code that makes computers fast.