I’m very interested in building tools for coding agents (where I spend a lot of my time).
I’ve been building https://smalldocs.org, it’s a simply styled, but functionally deep, artifact layer for agents. It’s open source and free to use.
Because it can incorporate charts, diagrams, spreadsheets, etc. in one artifact, it’s good for creating (and optionally sharing) agent created analysis.
It has a local library for all your Markdown files, and a cloud library (my one paid feature) too. Both support tagging and rich search/filtering. I find it turns SmallDoc artifacts into little checkpoints in work I can easily circle back to.
I think this is a very interesting article because it raises an idea I had not considered: these companies found PMF and huge growth through satisfy the demands of coders, it is interesting if they are in a bind where improving the model in one direction worsens it in others
You’re correct–you actually can’t improve the model in one area without changing the characteristics in every other area. It’s almost like the whole thing is just a lot of linear regression…
For a personal project a while ago I peeked at Gemini's reasoning tokens in their coding CLI. I was pretty shocked. It was a bit like discovering Marvin (from Hitchhiker's Guide) was hiding in there all along. There was a lot of concern about my needs, "The user want's us to respond in a simple way...", "The user wants a clean front end...". Under the hood the poor model seemed very anxious to please with a hint of depression. It was a bit sad to see!
For Qwen3.8, my first impression was weirdly Reni from TwoKinds: Immensely powerful but also apparently a ball of insecurities in the thinking traces.
(which makes sense, as I think one motivation for reasoning traces is to explore different options and approaches. So it makes sense that there is a lot "but wait, let me reconsider" in them)
What I found surprising is how strongly "logical contradictions" seem to influence the thinking trace. E.g. I had a situation where I accidentally copied a python file into a repo, but forgot to add a package that the file was depending on. Then I (somewhat carelessly) commited the file without ever testing it and gave the agent a task to work on the file.
If I had run it in Python, I'd have gotten an "cannot resolve import" error and that would have been the end of it.
Instead, the model went absolutely haywire.
The thinking traces were full of utter confusion how the file could possibly resolve its dependencies - but at the same time, entertaining the possibility that a committed file may have an error was apparently Verboten. Hence, the model wrote up ever more outlandish theories how the file could resolve that package and in the end started to make tool calls outside the repo to explore the entire file system before I stopped it.
Moral of the story: Underspecified requests are fine, but beware of anything self contradictory, it can easily send the Qwen model spiraling.
It's an interesting result (agents develop biases in their context) which reflects a lot of my experience working with agent, where I observe a lot of, what I kind of call, "context nudging" - where a droplet of an idea in an agent's context pushes its direction/output significantly. When it happens to me it always makes me question the type of intelligence LLMs provide.
It's cool that you're making a seemingly useful bit of software, but this reads like spam. "I had an agent summarize this" isn't particularly relevant to your opinion of it, unless you think the summary may have been misleading; so it comes across as a poor excuse to introduce your self-promotion.
I agree that "context nudging" is a thing. ChatGPT often seems to try really hard to connect ideas back to things I said earlier in the conversation even when it really shouldn't be relevant. But I would call that a matter of "wisdom" more than "intelligence".
> what I kind of call, "context nudging" - where a droplet of an idea in an agent's context pushes its direction/output significantly
I like this description. I constantly notice that how I ask a question strongly impacts the quality and technical merit of the answer I receive which similarly leads me to question any claims of generalization. It should go without saying that they're still incredibly useful tools when wielded properly.
Surprised it has not been mentioned, but I think relying on https://github.com/vercel-labs/skills is sound. It handles global installs for a wide range of coding agents. If I was working on an internal only skill I'd probably still use the same foundation.
Hmm maybe it always feels "different" to every generation going through automation of something previously un-automated, but don't you feel that to some extent this is different.
Humans have been the dominant species because of our ability to think, not our ability to get from A to B in any particular way/cook food. If we abstract ~the bulk of our thinking to LLMs, don't we lose something as a species which makes us more vulnerable... Or is it always the case during an automation?
Because I am LLM Dependent I got distracted reading the article and made my LLM summarise it to me in a few slides. For those in a rush/interested in LLM summary slop:
I do agree that we are on a pathway to losing much. If you focusing on maximising LLM-powered productivity you basically lose the learning that used to naturally happen when doing actual work. At the company I work for the focus is on productivity, and it's easy in many ways (less mental workload and I receive more praise) to go with it instead of fight it.
I enjoyed this article... random cat pictures (esp. thumbs up cat picture) kept me going.
I do think on device AI will be a BIG thing... and basically change how we interact with our computers entirely - you will just ask to get things done. Low latency, free to use as much as you want. I think this future is hard to avoid/this will be a chunk of the future.
Like Star Trek -- "tea. earl grey. hot.". Though there will still be tasks where you'd have to likely want or need to pick up a keyboard and mouse. But of course there will be plenty of casual users who dont want or need to do anything interesting and use it for simple tasks like ask for the weather, turn on lights, and brew their earl grey.
(I made a similar comment in a different post by Simon Willison yesterday about GPT Work)
I am unsure the direction that AI assisted document creation work will go in. I am opinionated and building something in the space. In this reply I discuss my view + what I have been building.
My opinion is: We now have AI, this new amazing flexible tool. We can throw it at any problem. But in terms of document creation we have a choice between old fashioned siloed document formats (.pptx, .pdf, .xlsx, .docx, etc.) and a freshly rendered - slightly randomly styled - html site with some basic javascript within it. I think these combinations are odd and do not maximise the powers of AI.
I've been building something called SmallDocs [0][1] which is a new take on work documents. It's all Markdown, but it always renders (100% privately) on the SmallDocs site. This means the SmallDocs renderer can convert recognised Markdown combinations into powerful traditional document formats [2]. For example, we convert a ```cells block into an in-line spreadsheet you can export to Excel.
This gives an agent the ability to express itself with a great deal of depth and flexibility. For example, your agent can produce a single data analysis "SmallDoc" with text, charts, spreadsheets, etc. [3] I find that combining formats quickens the speed I can understand a topic.
SmallDocs also have standardised and pretty boring styling. This means you spend less time understanding the layout of a flashy HTML page and more time on the content.
I also believe a pre-determined rendering engine has a lot of advantages over a freslhy built HTML artifact. It allows you to build in lot's of useful defaults that the agent doesn't have to code from scratch. These can be small things, for example, every time your agent renders a Markdown table in SmallDocs you can instantly copy it as a CSV or a PNG. And these can be bigger things, such as a rich slides DSL with solid styling defaults, which saves your agents a lot of tokens and results in consistently attractive HTML slides [4]. (Slides can also be automatically exported to .pdf and .pptx.)
I've been at it for a few months, and am still working on the project. I am looking for feedback and open source collaborators. Thanks for reading!
I’ve been building https://smalldocs.org, it’s a simply styled, but functionally deep, artifact layer for agents. It’s open source and free to use.
Because it can incorporate charts, diagrams, spreadsheets, etc. in one artifact, it’s good for creating (and optionally sharing) agent created analysis.
It has a local library for all your Markdown files, and a cloud library (my one paid feature) too. Both support tagging and rich search/filtering. I find it turns SmallDoc artifacts into little checkpoints in work I can easily circle back to.
Thanks for reading!
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