reedlaw

What do you mean? I'm Gen X but remember all those things. It seems like a reasonable mashup of nostalgia.

Since the Renaissance got started before the printing press, maybe you mean the press fueled it? The idea that the church found printing dangerous seems like a conflation with events that happened during the Protestant Reformation. The Catholic Church did censor works it found heretical, including unauthorized Bible translations.

One could argue the opposite conclusion, that technology helps break monopolies, but either view depends on reductionist historical readings. The truth is somewhere in between.

Good instinct. I did both. The script came out later.

I did the malware check using Claude, providing it with the same list (https://md.archlinux.org/s/SxbqukK6IA), and it did essentially the same things as this script does to verify. So either way should do the trick.

I also had an affected package installed, fortunately it was from the official repo before it was dropped and became an AUR package.

Yeah, the README looks like slop to me.

Codex is only slightly better, and that fluctuates so I switch back and forth.

I tried to create a hook that would detect when token usage was running out and write HANDOFF.md so I could switch to another agent and finish the current task. It never worked reliably. To make a hook for db queries, it would need to run before each bash call, check if it looks like a query, and then exit with a new prompt, e.g.: "Use tidewave's execute_sql_query for DB access". But then it could just ignore the prompt the same as CLAUDE.me. What if I really wanted to use bash for a specific task? The real issue is that prompts are not tightly coupled with capabilities. If we admit that, then skills are over hyped.

I'm skeptical skills will outperform training given that Opus 4.7 already ignores a 720-byte CLAUDE.md telling it to use tidewave (a Rails MCP server with 6 tools) for db queries. When I asked a new claude session about a record it called

Bash(DATABASE_URL=$(grep -E '^DATABASE_URL=' .env 2>/dev/null | head -1) echo "ok")

even though I have in CLAUDE.md:

For database queries, use tidewave first.

I then prompted:

use tidewave as per CLAUDE.md. also diagnose why you failed to heed that

● Diagnosis first: I defaulted to shell habits (env grep → psql) instead of pausing to recall the CLAUDE.md rule that tidewave is the first-line DB tool. The trigger was "look at this record" — I should have read that as "run a SQL query" and reached for tidewave immediately.

If Opus 4.7 doesn't follow simple CLAUDE.md instructions, I'm not sure what benefits other markdown files could bring. I don't trust Opus's own explanation, but it could point to the fact that the system prompt for bash is much longer than CLAUDE.md with tidewave.

While LLM judging could be helpful, I think the tool-call assertions (https://github.com/darkrishabh/agent-skills-eval#what-you-ge...) may be the most useful thing in agent-skills-eval given that it's the only objective measure of compliance.

Why do you say HashWithIndifferentAccess shows a lack of understanding? Like many Rails features, it's a convenience that abstracts away details that some find unpleasant to work with. Rails sometimes takes "magic" to the extreme through meta-programming. However, looking at the source [1], HashWithIndifferentAccess doesn't use eval, send, method_missing, or define_method. So I'm not sure how it seems weird to someone who works more with plain Ruby.

1. https://github.com/rails/rails/blob/fa8f0812160665bff083a089...

Chinese omits articles, verbs aren't conjugated, and individual characters carry more meaning than English letters, but other than those differences I don't have the impression that Chinese communication is inherently more concise. Some forms of official speech are wordy. Writing is denser, but the amount of information conveyed through speech is about the same. There are jokes about ambiguous words or phrases in both Chinese and English. So I was surprised at your take, but no objection to your points above. Ancient Chinese, on the other hand, is extremely concise, but so are other ancient languages like Hebrew, although in a different way. So it seems that ancient languages are compressed but challenging and modern languages have unpacked the compression for ease of understanding.

Are you saying Chinese is more concise than English? Chinese poetry is concise, but that can be true in any language. For LLMs, it depends on the tokenizer. Chinese models are of course more Chinese-friendly and so would encode the same sentence with fewer tokens than Western models.

I've come to the conclusion that Hacker News is the best aggregator out there. Substack knows my interests yet gives terrible recommendations. Youtube constantly recommends the same videos or exaggerates my interest in a topic based on a few views, spamming me with related content until I watch something unrelated. The only downside of Hacker News is that its focus is narrower than other sites. But perhaps because the focus is "Anything that good hackers would find interesting" there is a bias towards things I find interesting with less noise than more commercial offerings.

Claude has trained me on the use of the word 'invariant'. I never used it before, but it makes sense as a term for a rule the system guarantees. I would have used 'validation' for application-side rules or 'constraint' for db rules, but 'invariant' is a nice generic substitute.

Then I would say this is another proof that LLMs lack intellect or ability to reason about universals. See https://michaelmangialardi.substack.com/i/186405810/test-4-p...

Why is Haskell irrelevant to the argument that LLMs can't reliably permute programming knowledge from one language to another? In fact, the purity of the language and dearth of training data seems like the perfect test case to see whether concepts found in more mainstream languages are actually understood.

This is the second endorsement I've seen today. I gave OpenSpec a shot and was dismayed by the Explore prompt. [1] Over 1,000 words with verbose, repetitive instructions which will lead to context drift. The examples refer to specific tools like SQLite and OAuth. That won't help if your project isn't related to those.

I do like the basic concept and directory structure, but those are easy enough to adopt without all the cruft.

1. https://github.com/Fission-AI/OpenSpec/blob/main/src/core/te...

Do you have examples of the task maturation cycle? I'm not sure how it would work for tasks like extracting structured data from images. It seems it could only work for tasks that can be scripted and wouldn't work well for tasks that need individual reasoning in every instance.

How do you even begin to define objective measurements of software engineering productivity? You could use DORA metrics [1] which are about how effectively software is delivered. Or you could use the SPACE Framework [2] which is more about the developer experience.

1. https://cloud.google.com/blog/products/devops-sre/using-the-...

2. https://space-framework.com/

Plasma Bigscreen has been around for 6 years: https://itsfoss.com/news/plasma-bigscreen-comeback/

https://github.com/mickeynp/combobulate

Isn't this the premise behind Dilbert?

LLM-generated snippets of code are a breath of fresh air compared with much legacy code. Since models learn probability distributions they gravitate to the most common ways of doing things. Almost like having a linter built in. On the other hand, legacy code often does things in novel ways that leave you scratching your head--the premise behind sites like https://thedailywtf.com/

Whatever agent I tried would include thousands of tokens in tool-use instruction. That would use up most available context unless running very low-spec models. I've concluded it's best to use the big 3 for most tasks and qwen on runpod for more private data.

This seems like a perfect use case for a local model. But I've found in practice that the system requirements for agents are much higher than for models that can handle simple refactoring tasks. Once tool use context is factored in, there is very little room for models that perform decently.

Maybe a better way to handle "minimize cyclomatic complexity" would be to set an agent in a loop of code metrics, refactor, test and repeat.

I don't know about Opus, but Codex suddenly got a lot better to the point that I prefer it over Sonnet 4.6. Claude takes ages and comes up with half baked solutions. Codex is so fast that I miss waiting. It also writes tests without prompting.

Great feedback. On the Inverness to Gibraltar race the leaderboard has impossible times, including some negative numbers. According to Google Maps the best time is 1 day 8 hours, but this requires leaving Inverness at 6:44. The race starts at 9:00. Then the earliest arrival time in Paris is 22:19. According to Google Maps the best time from Paris to Gibraltar at this time of night is 1 day 2 hours. Despite all this, there are several 1 day records. The best I can do is 2 days 5 hours. Of course I may be missing some better route, but I suspect cheating, especially from names like AdolfHitler.

I'm using the Aristotelian definition of my linked article. To understand a concept you have to be able to categorize it correctly. LLMs show strong evidence of this, but it is mostly due to the fact that language itself preserves categorical structure, so when embedded in geometrical space by statistical analysis, it happens to preserve Aristotelian categories.

Yes, LLMs mimic a form of understanding partly through the way language embeds concepts that are preserved when embedded geometrically in vector space.