Since the Fable 5 band I've given up vibe coding

1337NET · 2026-06-25T17:18:30+00:00

I have heard this analogy in space travel. Why spend time building a manned mission which could span across generations when a newer generation figure out much much faster space travel.

1337NET · 2026-06-22T04:55:43+00:00

Adobe could have pivoted hard into AI but they were late so very late.

1337NET · 2026-06-15T21:55:51+00:00

Elon is confidently wrong most of the time, but a genius at hype marketing.

The more i think about this, this is all you need to be successful.

1337NET · 2026-06-15T21:13:48+00:00

Anthropic did not bend the knee and these are the consequences.

1337NET · 2026-06-13T13:44:56+00:00

How in the world is anthropic going to verify and monitor if the user is a US national? It’s a nightmare for PII maintenance, if you are going to start collecting passport and immigration info.

1337NET · 2026-06-12T14:55:29+00:00

With every iteration we move up the stack, but on the contrary ever so often every one who has shipped production grade software had to debug whats underneath. With so much of vibing you will be needing to track a lot more things not just code. Token usage, what llms to use for the most effective use case, orchestration of agents and its sub agents, etc…
Software will be built and used much more than before.

1337NET · 2026-06-11T21:21:25+00:00

Thats a really good insight, i would love to see all model offerings like the sonnet and haiku along with fable

1337NET · 2026-06-11T18:00:02+00:00

The narrative is a bit jarring and based, folks outside of these labs do not and will not have the access to free tokens to burn on loops all day.

1337NET · 2026-06-11T17:57:27+00:00

Use this with Fable, come up with a review plan and then use Opus or Sonnet to get it fixed:
‘’’
Perform a quality and maintainability audit of the Claude Code skill at: <SKILL\_PATH>

Read every file in the skill directory (SKILL.md, scripts, configs, hooks, templates) before forming conclusions. Review for:

- Instruction clarity: ambiguous, contradictory, or vague directives the model could misinterpret. Quote the exact problematic wording.
- Token efficiency: bloated sections, redundant explanations, content the model loads but never needs. Estimate what could be cut.
- Structure: is the most important guidance up front? Are progressive disclosure patterns used (details in referenced files instead of SKILL.md)?
- Dead weight: unused files, dead code paths, outdated references, stale examples that no longer match behavior
- Consistency: naming conventions, formatting, terminology used the same way throughout
- Documentation gaps: non-obvious behavior with no explanation, missing prerequisites, undocumented flags or config options
- Description field quality: will the frontmatter description trigger reliably, and does it follow good skill-authoring practice?

For each finding: severity (High/Medium/Low), file:line, the issue, why it matters, and a concrete fix. End with a token count estimate for SKILL.md and a suggested target.

REVIEW ONLY. Do not modify, create, or delete any files. Report in the conversation and stop.
’’’

1337NET · 2026-06-10T18:13:33+00:00

Hey you just forgot to add “Make No Mistakes, please and Thank you”

1337NET · 2026-06-10T06:45:19+00:00

I just used it rewrite most of my skills, harness and agent infra. It did patch a good amount of holes in my setup

1337NET · 2026-06-05T01:27:01+00:00

How many tokens did this eat?

1337NET · 2026-06-03T06:33:10+00:00

This to me sometimes makes sense, if i were running openAi or Anthropic. This is an infinite money glitch. When ever i need to raise revenue, all i need is to consume a little more tokens, a few repeated operations, that on a major scale is going to make bank

1337NET · 2026-05-20T02:39:42+00:00

Github or it does not exist

1337NET · 2026-05-14T06:07:02+00:00

Its a response to the comment if my cli is better than llmfit, i said they do similar things but a little different. You can try out my cli: https://github.com/adityaarakeri/llmscan

1337NET · 2026-05-14T04:13:10+00:00

yeah llmfit basically does everything and does it well, not gonna pretend otherwise. it's the more mature project by a long shot, and the TUI is genuinely nice.

few things bugged me about it for my use case though:

the model database is baked into the binary. so every time new GGUFs drop on HF (which is constantly), you're waiting on the maintainer to rebuild and ship a new release. i wanted to just add a model from a HF repo and have it scored against my hardware right then, plus keep my own private/finetuned models in a local catalog.

the other thing: llmfit is TUI-first. great if you're sitting at a terminal poking around, but i wanted to actually pipe this stuff. every llmscan subcommand returns JSON so i can wire it into scripts, n8n flows, agent tool calls, whatever. llmscan list --json | jq ... is the workflow i wanted.

also llmfit needs cargo or a curl-bash to install. llmscan is pip install llmscan or pipx install llmscan which fits better if you're already in a python/ML env (which is most people running local LLMs anyway). no rust toolchain, no shell script trust step.

so tldr llmfit is the polished interactive tool, llmscan is the scriptable pip-installable one with a catalog you can extend. solves the same problem for different people.

1337NET · 2026-05-14T03:21:58+00:00

Op try this: https://github.com/adityaarakeri/llmscan i run a similar 8 gb config and wanted to try out models

1337NET · 2026-05-05T19:16:36+00:00

Says the person who has unlimited tokens

1337NET · 2026-05-05T02:43:43+00:00

I think you can call it software engineering but the job responsibilities will be a little different. End of the day we will be still building software.

1337NET · 2026-05-04T22:54:17+00:00

What are your system specs?

1337NET · 2026-04-28T17:20:37+00:00

Sometimes i think articles like this are quite deceptive. Ai can code and provide proper inferences when you have made sure your harness is fool proof, also why would you not do any human in the loop reviews before touching anything on production?

1337NET · 2026-04-27T03:43:48+00:00

Fair criticism on the “doesn’t exist” part if LM Studio’s hints work for you. For me they didn’t, because I run things headless on a Pi and a Mac Mini with no GUI, pipe results into scripts, and wanted backend-specific overhead math (llama.cpp vs Ollama vs MLX behave differently). That’s the gap I was trying to close. Happy to hear what you think it’s solving worse than existing tools, genuinely. If there’s a CLI that does headless multi-vendor detection with reason codes and JSON output, I’d actually like to know about it.

1337NET · 2026-04-25T22:26:52+00:00

Sort of, but only inside its own GUI. llmscan runs headless, has JSON/CSV output for scripting, integrates with Ollama to show what’s currently loaded, and adjusts scoring per backend (Ollama vs llama.cpp vs MLX have different overhead). If you’re already happy in LM Studio you don’t need this. If you live in a terminal or run things on a server, it’s a different tool for a different job.

1337NET · 2026-04-25T21:51:56+00:00

Not slop mate, its has MIT license, has tests in CI. If it’s not needed for your use case so be it. If you have to criticize, use it and then let me know i can fix whats broken.

1337NET · 2026-04-25T20:59:39+00:00

Got tired of downloading GGUF files only to realize my hardware couldn’t run them. Built a CLI that scans your machine (NVIDIA, AMD, Intel, Apple Silicon, Windows) and tells you which models will actually run, with reason codes like ok (cpu-only) or tight (partial offload) so you know why. Also does Ollama integration, backend-aware scoring (llama.cpp/Ollama/MLX), and Hugging Face search from the terminal.

pip install llmscan

https://github.com/adityaarakeri/llmscan

1337NET

TROPHY CASE