16h ago · 13 min read · A hands on guide for AI engineers who care about embedding compression, retrieval latency, and not having to rebuild their index every time the data shifts. If you build retrieval augmented generatio
Join discussionMay 9 · 7 min read · Running LLMs on-device means fighting two constraints simultaneously: memory and latency. The KV-cache — the buffer that stores past token representations so the model does not recompute them — is often the bottleneck on both fronts. A paper publishe...
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May 4 · 9 min read · AI sustainability keeps getting framed around the wrong question: does a local LLM use less electricity and water than ChatGPT or Claude? The sharper question is: how many frontier-model calls are we
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Apr 26 · 5 min read · The April 2026 Private AI Revolution: Running DeepSeek-R2-7B at 60 tokens/sec on Your Gaming Rig Real numbers: I'm getting 61.4 tokens per second on a consumer RTX 4080 with DeepSeek-R2-7B. Cost? $0.00/month vs $240/month cloud inference. Zero depend...
Join discussionApr 26 · 18 min read · Originally published at Gothar Tech Part of our 2025 software architecture series. Edge AI Inference: Running Models at the CDN Layer The fastest inference call is the one that never crosses an ocean. For two decades, CDNs existed to cache bytes: i...
Join discussionApr 18 · 6 min read · If you've been running local LLMs, you already know the drill: download a 70B model, quantize it to 4-bit with GPTQ or GGUF, cross your fingers, and hope your GPU doesn't catch fire. It works. It's practical. But there's a fundamentally different app...
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Apr 6 · 11 min read · Part 1 of 2. Why We Did This Hammer.ai runs a industrial research lab hyper focused on regulated domain document understand at extremely efficient margins. Private equity self funded companies like f
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Mar 25 · 5 min read · One weekend in my hostel room, I found myself spiraling into a question that felt both obvious and oddly underexplored: if data is everything, and memory is the channel through which intelligence flow
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Mar 19 · 7 min read · GLM-4.7-FP8 is one of the latest models focused on this new generation of developer-centric AI. Developed by Z.ai, GLM-4.7 introduces improvements in agentic coding, reasoning, and tool usage, while t
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