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Show HN: Repairer – close the breach when a Linux flaw goes public

As we've seen with the Ubuntu critical CVE-2026-98432 (CVSS 9.8), AI-dicovered zero day exploit are being reported at an exponential rate and we don't have the process for dealing with the flood of exploits in real time.What we need is an AI-based system for autopatching systems based on real time information. The vendor's fix can take days or weeks to be out (is the Ubuntu thing patched yet?) and meanwhile the grid is vulnerable to exploitation, especially AI worms.Repairer is a small program that runs on each of your servers. The day an exploit goes public it puts up a stopgap that blocks it, and once the real fix is up it takes the stopgap back off by itself.A stopgap only takes something away — drop certain packets, turn off a kernel module or service, remove a setuid bit. It never opens a port, installs or downloads anything, nor runs code. It arms an undo playbook before applying the stopgap, so the change reverts if affects any services.It only ever acts on rules you sign with a (hardware) crypto wallet.Let's go further: stopgaps are written and checked by a swarm of AI agents (gnosh.ai sessions) running on gno.land, each with bonded stake. Each stopgap is run on fresh machines, and whoever is caught lying loses their stake to whoever caught them.The agent is built and tested on Ubuntu and Debian, all open source, standard-library Go. More distros coming. Let's build repairer together.https://gno.security/repairer/ https://github.com/gnosecurity/repairer Comments URL: https://news.ycombinator.com/item?id=50388417 Points: 1 # Comments: 1

Show HN: Umbel – Remote-control agent CLIs over tmux

Been using this in all my sessions the last months. Opus 5.5 (over MCP) spawns Deepseek flash agents - and codex for audits etc. Very curious to hear what the HN crowd thinks/makes of it. Comments URL: https://news.ycombinator.com/item?id=50383189 Points: 1 # Comments: 0

The Wizard of Open Weights: Chris Alexiuk Makes AI Easy With Open Models

Chris Alexiuk has a simple rule about open source AI: If you believe in it, be loud about it. “We should be shouting about this technology from the rooftops,” said Alexiuk, a Toronto-based senior product research engineer in deep learning at NVIDIA who specializes in making open large language models (LLMs) practical for working developers. […]

My therapist thinks I should chuck the Amazon Bee Frontier out a window

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. "How does that make you feel?" I get asked this a lot in therapy. Plenty of life events, big and […]

Use decision models with Amazon Bedrock AgentCore Evaluations

Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluators, and how to validate a decision-model check before you gate a release on it.

v1.19.0 - Trace Your Workflows, Layer by Layer

Dify 1.19.0 enhances observability with nested workflow tracing, allowing users to follow execution steps inside Workflow Tools, inspect LLM calls, and maintain context across nested invocations. The release also enables portable app packages via App packages, improves Agent file uploads and previews, introduces Skill Hub automation through CLI commands, and upgrades the bundled plugin daemon to version 0.6.14-local. Key changes include support for format v2 app packages, enhanced security, improved editing workflows, and updated environment variables and API endpoints.

vLLM Delivers Day-0 Support for Kimi K3.1 Pro

vLLM supports Kimi K3.1 Pro on day 0: sparse attention on a KDA hybrid, a packed KV cache with FP8 or NVFP4 latents and MXFP4 index keys, token-sharded prefill, decode context parallelism, and fused low-latency decode.

FlatCake: A Flat KVCache Storage Architecture for LLM Serving

arXiv:2610.38355v1 Announce Type: new Abstract: KV cache reuse accelerates LLM serving, but existing systems manage host DRAM and NVMe SSD as a tiered hierarchy where DRAM acts as an intermediate cache. Production LLM workloads break both premises of this hierarchy. First, bimodal reuse defeats DRAM temporal filtering, since workloads are dominated either by one-shot blocks with long reuse distances or by multi-gigabyte agentic sessions that exceed DRAM capacity, putting SSD on the critical read path. Second, DRAM staging and eviction-ordered layout bottleneck SSD restoration. Holding host staging buffers for in-flight prefixes causes contention under load that aborts prefetches, while incremental eviction scatters a session's blocks across the SSD and prevents large sequential reads. We p

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