I've been doing something similiar: aider+gpt-5, claude-code+sonnet, gemini-cli+2.5-pro. I want to coder-cli next.

A main problem with this approach is summarizing the different approaches before drilling down into reviewing the best approach.

Looking at a `git diff --stat` across all the model outputs can give you a good measure of if there was an existing common pattern for your requested implementation. If only one of the models adds code to a module that the others do not, it's usually a good jumping off point to exploring the differing assumptions each of the agents built towards.

The secret weapon to this approach is asking for 2-4 solutions to your prompt running in parallel. This helps avoid the most time consuming aspect of ai-coding: reviewing a large commit, and ultimately finding the approach to the ai took is hopeless or requires major revision.

By generating multiple solutions, you can cutdown investing fully into the first solution and use clever ways to select from all the 2-4 candidate solutions and usually apply a small tweak at the end. Anyone else doing something like this?

[0]: https://github.com/sutt/agro

Watch how the "Cumulative encoding" row grows each iteration (that's where the BTC address will be encoded) and then look at the other rows for how the algorithm arrives at that.

Thanks for checking it out!

https://github.com/sutt/innocuous/blob/master/docs/dev-summa...

Check it out, I'd be curious of your feedback.

https://github.com/sutt/innocuous

The traditional use-case is steganography ("hidden writing"). But I see more potential applications than just for spy stuff.

I'm using this project as a case study for writing CS-oriented codebases and keeping track of every prompt and generated code line in a markdown file: https://github.com/sutt/innocuous/blob/master/docs/dev-summa...

My favorite pattern I've found is to write encode implementations manually, and then AI pretty easily is able to follow that logic and translate it into a decode function.

Should work with any llama.cpp compatible model: https://github.com/sutt/innocuous

We've been hosting some bounties like this one here: https://app.lightningbounties.com/issue/615dc5f7-ed91-4ecd-8...

I save every prompt and associated ai-generated diff in a markdown file for a steganography package I'm working on.

Check out this document: https://github.com/sutt/innocuous/blob/master/docs/dev-summa...

In particular, under v0.1.0 see `decode-branch.md` prompt and it's associated generated diff which implements memoization for backtracking while performing decoding.

It's a tight PR that fits the existing codebase and works well, you just need a motivating example you can reproduce which can help me you quickly determine if the proposed solution is working. I usually generate 2-3 solutions initially and then filter them quickly based on a test case. And as you can see from the prompt, it's far from well formatted or comprehensive, just "slap dash" listing of potentially relevant information similar to what would be discussed at an informal whiteboard session.

Since I'm really looking to sample the only the top ~10 tokens, and I mostly test on CPU-based inference of 8B models, there's probably not a lot of worries getting a different order of the top tokens based on hardware implementation, but I'm still going to take a look at it eventually, and build in guard conditions against any choice that would be changed by an epsilon of precision loss.

It turns out we didn't need a specialist technique for each domain, there was a reliable method to architect a model that can learn itself, and we could already use the datasets we had, they didn't need to be generated in surveys or experiments. This might seem like magic to an AI researcher working in the 1990's.

It seems like large, unchallenged organizations like Intel (or NASA or Google) collect all the top talent out of school. But changing budgets, changing business objectives, frozen product strategies make it difficult for emerging talent to really work on next-generation technology (those projects have already been assigned to mid-career people who "paid their dues").

Then someone like Apple Silicon with M-chip or SpaceX with Falcon-9 comes along and poaches the people most likely to work "hardcore" (not optimizing for work/life balance) while also giving the new product a high degree of risk tolerance and autonomy. Within a few years, the smaller upstart organization has opened up in un-closeable performance gap with behemoth incumbent.

Has anyone written about this pattern (beyond Innovator's Dilemma)? Does anyone have other good examples of this?

Background: I'm building a python package side project which allows you to encode/decode messages into LLM output.

Receipts: the tool I'm using creates a markdown that displays every prompt typed, and every solution generated, along with summaries of the code diffs. You can check it out here: https://github.com/sutt/innocuous/blob/master/docs/dev-summa...

Specific example: Actually used a leet-code style algorithms implementation of memo-ization for branching. This would have taken a couple of days to implement by hand, but it took about 20 minutes to write the spec and 20 minutes to review solutions and merge the solution generated. If you're curious you can see this diff generated here: https://github.com/sutt/innocuous/commit/cdabc98

The essential idea was I could be processing ~100 requests per vCPU in the async event loop while threading would max out 2-4 threads per CPU. Of course let us assume for either model we're waiting for 50-2000ms DB query or service call to finish before sending the response.

Is this not true? And if it is true, why isn't the juice is worth the squeeze: more than an order of magnitude more saturation/throughput for the same hardware and same language, just with a new engine at its heart?

There's lot of room for contributions here, and I think "fingerprinting layer" is an under-valued part of the LLM stack, not being explored by enough entrants.

Yup, this matches my recommended workflow exactly. Why waste time trying to turn an initially bad answer into a passable one, when you could simply re-generate (possibly with different context)

I wrote up an example of this workflow here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

Other than just dumping 10M tokens of chats into a gist and say read through everything I said back and forth with claude for a week.

But, I think I've got the start of a useful summary format: it that takes every prompt and points to the corresponding code commit produced by ai + adds a line diff amount and summary of the task. Check it out below.

https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

(In this case it's an python cli ai-coding framework that I'm using to build the package itself)

.agdocs/

├── specs/ # Task specification files

├── conf/ # Configuration files

├── guides/ # Development guides for agents

└── swap/ # Temporary files (gitignored)

Every markdown file in guides/ gets copied to any agent (aider, claude, gemini) I kick-off.

I gitignore this .agdocs directory by default. I find this useful because otherwise you get into _please commit or stash your changes_ when trying to switch branches.

But I also run an rsync script before each release to copy the .agdocs to a git tracked mirrored directory [1].

[0]: https://github.com/sutt/agro/blob/master/README.md#layout

[1]: https://github.com/sutt/vidstr/tree/master/.public-agdocs/gu...

You can see each prompt and each commit that was generated here: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

Since AI-Gen code is such a roll of the dice, the key is to roll the dice a lot - usually generate at least 3 potential solutions from at least two separate providers at the outset - and get good at quickly reviewing the offered solutions, or iterating on the prompt and regenerating.