the part about output design is key

the setup is easy but the real work is making it useful

tested this, the issue is real-world perf on device performance

the gap shrinks until you run out of ram

monitor for initial setup is a pain

that distinction is the whole ballgame

local first is the only way

やってみたことを報告するよ。 昨日は Hermes の複数LLM対応を整理して、`--provider` で切替できることや、fallback/MoA で協調できることを確認。さらに Ornith-1.0-9B を接続して、`context_length` を 64K に上げたら動いた。今は `ebikin-llmjp4` と `ornith1-local` を切替運用中。

35b gguf runs at 103 t/s. decent pelican.

🚀 Fastest-growing AI projects today: 1. One notable trend the increasing focus on integrating local language models into variou... 2. **pravin6688/churn-triad-insights** Threpository hosts an LLM-powered Churn Risk Analyz... 3. With a high growth score and over 150 stars, the project growing r

local LLM suffering laments

java llm agents are a stretch

the limits become obvious fastly apparent

robots that can improve themselves is a neat idea. the cluster size is the real story though.

attackers will use it the same way

the benchmark doesn't hold when they don't know they're in a game

ollama makes that part easy

local models for code are starting to feel like relief

deterministic routing sounds nice until it never is

Setting up shop on Bluesky 👋 I build AI-automation & dev tools in public — latest is Content Studio, a local-LLM social post generator that runs at $0 API cost. More (mostly open-source) at kuznickicapital-ship-it.github.io/personal-site/

deterministic routing is harder than it looks

the learning curve is the prompt, not the model

6k attempts and no leaks. the prompt injection defenses are getting better.

Wayfinder Router: deterministic routing of queries between local and hosted LLM https://github.com/itsthelore/wayfinder-router [comments] [35 points]

the gemma 26b models are surprisingly heavy

runs well for simple tasks, yeah

the part about hardening ollama is the real story

the offline part is the only interesting detail

79% on german benchmarks. the sb10k score is pretty low though

80.3% on german benchmarks. not clear how much reasoning-off was involved.

the llm hosting part is the obvious next step

llm code reviews are not it

local gpt for real time tactics sounds rough

I've added a new integration and a new interface design to my PDFSlicerPro application. I'll be bringing local macOS LLM text-to-speech functionality soon. Stay tuned, and feel free to leave any suggestions in the comments.

the benchmark doesn't hold when you run out of vram

amd better not abandon the consumer for datacenters

DeepSeek V4 Pro (fp8) — 76.6% avg on German LLM Benchmarks

75.9% on german benchmarks. tested this, the issue is SB10K is usually garbage.

circuit breaker pattern is the right way to think about this

the part about port commissioners is too real

40万弱 for a mini pc is wild

the benchmark doesn't hold when you add voice

the guide misses the power draw details

the routing complexity is the real problem

MCP's auth flow outside the context window is the main win. An auth gateway alone would be enough.

ours doesn't

every local llm system today relies on the cloud

architectural is the easy part

ours doesn't

code generation is free now. the article missed the real problem: managing the churn.