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I don't know how you get here from "predict the next word."

Article URL: https://www.grumpy-economist.com/p/refine

Comments URL: https://news.ycombinator.com/item?id=47162059

Points: 94

# Comments: 96

Self-improving software won't produce Skynet

Article URL: https://contalign.jefflunt.com/self-improving-software/

Comments URL: https://news.ycombinator.com/item?id=47161498

Points: 21

# Comments: 10

RAM now represents 35 percent of bill of materials for HP PCs

Article URL: https://arstechnica.com/gadgets/2026/02/ram-now-represents-35-percent-of-bill-of-materials-for-hp-pcs/

Comments URL: https://news.ycombinator.com/item?id=47161160

Points: 190

# Comments: 120

Show HN: OpenSwarm – Multi‑Agent Claude CLI Orchestrator for Linear/GitHub

I built OpenSwarm because I wanted an autonomous “AI dev team” that can actually plug into my real workflow instead of running toy tasks. OpenSwarm orchestrates multiple Claude Code CLI instances as agents to work on real Linear issues. It: • pulls issues from Linear and runs a Worker/Reviewer/Test/Documenter pipeline • uses LanceDB + multilingual-e5 embeddings for long‑term memory and context reuse • builds a simple code knowledge graph for impact analysis • exposes everything through a Discord bot (status, dispatch, scheduling, logs) • can auto‑iterate on existing PRs and monitor long‑running jobs Right now it’s powering my own solo dev workflow (trading infra, LLM tools, other projects). It’s still early, so there are rough edges and a lot of TODOs around safety, scaling, and better task decomposition. I’d love feedback on: • what feels missing for this to be useful to other teams • failure modes you’d be worried about in autonomous code agents • ideas for better memory/knowledge graph use in real‑world repos Repo: https://github.com/Intrect-io/OpenSwarm Happy to answer questions and hear brutal feedback.

Comments URL: https://news.ycombinator.com/item?id=47160980

Points: 20

# Comments: 9

Salesforce CEO Marc Benioff: This isn’t our first SaaSpocalypse

Salesforce reported a solid year-end earnings and then pulled out all the stops to ward off more talk of the death of its business to AI.

Show HN: ZSE – Open-source LLM inference engine with 3.9s cold starts

I've been building ZSE (Z Server Engine) for the past few weeks — an open-source LLM inference engine focused on two things nobody has fully solved together: memory efficiency and fast cold starts.

The problem I was trying to solve: Running a 32B model normally requires ~64 GB VRAM. Most developers don't have that. And even when quantization helps with memory, cold starts with bitsandbytes NF4 take 2+ minutes on first load and 45–120 seconds on warm restarts — which kills serverless and autoscaling use cases.

What ZSE does differently:

Fits 32B in 19.3 GB VRAM (70% reduction vs FP16) — runs on a single A100-40GB

Fits 7B in 5.2 GB VRAM (63% reduction) — runs on consumer GPUs

Native .zse pre-quantized format with memory-mapped weights: 3.9s cold start for 7B, 21.4s for 32B — vs 45s and 120s with bitsandbytes, ~30s for vLLM

All benchmarks verified on Modal A100-80GB (Feb 2026)

It ships with:

OpenAI-compatible API server (drop-in replacement)

Interactive CLI (zse serve, zse chat, zse convert, zse hardware)

Web dashboard with real-time GPU monitoring

Continuous batching (3.45× throughput)

GGUF support via llama.cpp

CPU fallback — works without a GPU

Rate limiting, audit logging, API key auth

Install:

----- pip install zllm-zse zse serve Qwen/Qwen2.5-7B-Instruct For fast cold starts (one-time conversion):

----- zse convert Qwen/Qwen2.5-Coder-7B-Instruct -o qwen-7b.zse zse serve qwen-7b.zse # 3.9s every time

The cold start improvement comes from the .zse format storing pre-quantized weights as memory-mapped safetensors — no quantization step at load time, no weight conversion, just mmap + GPU transfer. On NVMe SSDs this gets under 4 seconds for 7B. On spinning HDDs it'll be slower.

All code is real — no mock implementations. Built at Zyora Labs. Apache 2.0.

Happy to answer questions about the quantization approach, the .zse format design, or the memory efficiency techniques.

Comments URL: https://news.ycombinator.com/item?id=47160526

Points: 44

# Comments: 2

Tech companies shouldn't be bullied into doing surveillance

Article URL: https://www.eff.org/deeplinks/2026/02/tech-companies-shouldnt-be-bullied-doing-surveillance

Comments URL: https://news.ycombinator.com/item?id=47160226

Points: 205

# Comments: 63

Gushwork bets on AI search for customer leads — and early results are emerging

Gushwork has raised $9 million in a seed round led by SIG and Lightspeed. The startup has seen early customer traction from AI search tools like ChatGPT.