An AI Transformation team at a Fortune 500 financial services firm told me they’ve shipped 19,000 skills across 175 unique Coder workspace types in six months. That’s not a headcount story. It’s proof that as AI intelligence becomes a commodity, the real competitive advantage shifts to how businesses codify and distribute their proprietary workflows.
A challenge to a coding agent: prove whether months of homegrown test harnesses and bakeoff scripts were reinventing wheels that already exist. A survey of what hobbyists, techtubers, and the AI benchmarking industry already publish, and an honest verdict on this blog’s own tooling habits.
I put Qwen 3.6 27B, Qwen 3.6 35B-A3B, Qwythos-9B, GLM-4.7-Flash, and Nemotron-3-Nano through the same real coding task on my homelab RTX 5090. Along the way I had to live-patch two separate llama.cpp bugs — and even after fixing them, I couldn’t fully prove one model’s failure wasn’t the harness’s fault.
Back in Q1 I read a headline about Google cutting AI memory use 6x and filed it under “watch and revisit.” Four months later, Google still hasn’t shipped official code, but a whole ecosystem of forks has, llama.cpp has an open PR, and at least one compatibility gotcha lands squarely on our daily driver. Here’s the honest state of TurboQuant heading into Q3, and the test I’d actually run against it.
A routine “update Coder” request turned into a full model bakeoff: fixing misconfigured thinking params so Sonnet 5 would stop calling itself 4.5, discovering Playwright MCP can’t be wired into Coder Agents at all, and watching three frontier models independently pause at the exact same step before finishing. Sonnet 5 won on score and on cost, by a mile.
A gloomy Cape Cod afternoon post-July 4th turns into a deep dive on ComfyUI, Lemonade Server, and LocalAI — plus llama-benchy and AMD’s AI Playbooks — and the case for a bakeoff against our hand-tuned llama.cpp stack.
GLM is suddenly everywhere in developer conversations. Before we run the bakeoff, we need to answer two questions: what is GLM, and is it suitable for a single RTX 5090 homelab?
A CEO panel at an AI event sparked a simple but powerful question every startup founder should ask themselves: does your business get better as AI models improve, or does it get worse?
I gave five local LLMs and one frontier cloud model the same coding task on my homelab: build a tag manager for the blog’s admin panel. Only two shipped anything. Here’s what happened.
Four frontier models, ten tasks, one government shutdown. We ran Claude Fable 5 through the homelab benchmark harness three hours before Anthropic pulled the plug — and it came in second. Here’s the full bakeoff.
Two Discord bots, one 14B model, five fitness-tracker tasks. Both agents failed on the first try. Getting them working required debugging context overflow, silent tool parameter drops, and a chat template flag that changes everything. The results reveal as much about the state of local AI agents as they do about which framework won.
A model refresh on the homelab (Qwen 3.6, new embeddings, 469 llama.cpp builds), a feature sprint on the vacation planning site (calendar sync, expense tracking, and three bugs that taught us more than the features did), and automating Substack syndication after discovering two more undocumented quirks. Three unrelated workstreams, one theme: maintenance is where the real learning happens.
I’ve been running OpenClaw on the homelab for a month. A recommendation sent me down the Hermes Agent rabbit hole — and the research before the first real test revealed my daily driver model was broken for tool calling all along.
DeepSeek V4-Pro, V4-Flash, and Zyphra ZAYA1 are three of the most exciting new models in local AI. None of them run on our RTX 5090 homelab — for completely different reasons. Here’s the research, the math, and what it means for anyone building a local inference rig.
Two AI models got the same prompt: review the blog fodder, check for redundancy, and draft a post. Opus chose a debugging war story. Qwen chose a data-driven redesign. Neither picked the same fodder. Here’s what the difference reveals about how models think about content.
We ripped out Ollama, migrated to llama.cpp, and benchmarked five local models across 12 tasks on an RTX 5090. The results surprised us — and the winner wasn’t who we expected.
Gemma 4 failed to build a single feature in our last test. This time we diagnosed the problem, switched from Ollama to llama.cpp, tuned the inference settings, and Gemma shipped a working search feature to production. Then Opus reviewed the code and made it better. Here’s what we learned about making local models actually work.
We pitted Gemma 4 against Opus 4.6 on a real feature build for vibescoder.dev. Gemma is the fastest model in our benchmark. It also couldn’t finish the job. Here’s what happened when we stopped testing toy apps and started building production code.
We added Google’s Gemma 4 and Moonshot’s 1-trillion-parameter Kimi K2 to the local model benchmark. Five out of six models scored perfect. Gemma 4 is the new speed king. And yes, we ran a 579 GB model off an NVMe drive — at 0.6 tokens per second.
We gave six LLM models the exact same coding prompt and measured everything: speed, tokens, and whether the code actually works. Three models scored perfect. Two built the wrong kind of app. One ran out of tokens mid-line.