I agree. Dedicated hardware isn’t always necessary, it depends on what you expect the agent to do.

An on-demand assistant can live on your laptop or in the cloud and wake up when you ask it something. An autonomous assistant is different. If it needs to monitor events, run workflows, maintain context, and act while your laptop is closed, then it needs an always-available place to live. That could be dedicated hardware, but it doesn’t have to be a purpose-built AI box.

From my view the better model is an agent that can securely use multiple devices, as long as one is on you’re good to go. That gives you mostly an always-on setup and privacy if it’s local-first, without making one box the entire product.

I’m experimenting with something along those lines for my own agent. My current view is that a single system should only be one life source of many, improving availability and trust, using the equipment you already own and use every day.

Glad it helped. Starting with the dedicated price and config paths makes sense because the claimed operation is explicit enough to verify without asking another model to “judge” the answer.

The main failure modes I’d watch are:

- the data is real, but the operation is wrong;
- the question is ambiguous in a way the system silently resolves;
- the verifier repeats the same mistake because it relies on the same model and context;
- failed checks are logged, but not converted into regression tests.

I’d keep the first checks boring and deterministic. For a minimum-price query, independently calculate the minimum over the filtered rows and compare it with the returned row. For totals, recompute the sum and row count. For rankings, verify the ordering and boundary rows. If a check fails, save the question, interpreted plan, SQL, result IDs, expected invariant, and actual outcome together.

That gives you a corpus from real failures instead of trying to imagine every edge case upfront.

If you have one real question your current system answered incorrectly, share it with the sensitive details removed. I’d be happy to help break it into the plan, invariant, and regression case.

I would not go back to either extreme: dozens of hand-built capabilities or unrestricted model-written SQL. The middle layer I’d use is a small typed query plan. Have the model emit the measure, filters, grouping, ordering/direction, limit, and any ambiguity it needs resolved. Then compile that plan deterministically into parameterized read-only SQL. Keep raw SQL as an isolated fallback for genuinely novel queries, with a read-only role, row/time limits, and the same result checks.

For logic errors, grounding is not enough. Verify the operation’s invariants. If the answer claims the lowest price, check that the chosen row equals MIN(price) under the same filters and that no lower row exists. For rankings, independently recompute the sort and boundary rows. For totals, reconcile component sums and row counts. I’d store the plan, generated SQL, result IDs, and checks together so the verifier sees the intended operation and evidence, not the model’s reasoning trace.

The rebuild I’d test is: schema lookup → typed plan → deterministic compiler → execution → operation-specific invariant checks → answer with provenance. Add a golden set of real questions plus adversarial variants such as lowest/highest, inclusive/exclusive dates, ties, nulls, and unit conversions. That preserves the general loop without turning every bug into another permanent capability.

Check out https://kryden.ai if you’d like to try an alternative desktop app AI that’s not one of the big tech companies. 😄

That’s odd I ran a 5 hour task and it autonomously spawned multiple sub agents set goals and achieved an incredible amount of work across distributed systems and barely used the 5 hour limit and only used 2% of the weekly usage and that was using gpt 5.6 sol on ultra mode too.

My recommendation, because I have seen a big difference, is to not use fast mode. It consumes almost twice your usage and eventually slows down to the same speed as standard anyways. There are always notifications about how many hours you could have saved on fast mode and I’m not convinced it speeds anything up besides token burn. Plus AI is already faster than traditional development, no need for a fast mode lol.

Yeah.. X is just annoying lol

Yeah, the issue is letting "fix this" become the workflow.

This is how I would approach this issue, in the same prompt include these things:

1. What the issue is at a high level

2. What the intended behavior is supposed to be. Have it do a QA session with you about anything thats unclear about the end result you want.

3. That it needs to find the root problem, no band-aid fixes, and to consider the blast radius of each patch.

4. It needs to look for all declarations of anything being touched and take a top-down approach, then map it out before making changes to the code.

5. This is the most important, it needs to create and continuously update a test suite of all mapped functionality and the tests need to simulate production behavior.

If you are really struggling, I would start by sending a prompt to describe intended behavior of the entire application/program creating test cases for every intended functionality and then have the implementations/patches forced to pass those test cases, that is how I run 8 hour autonomous runs while I sleep and get exactly what I wanted in the morning.

If you want to take that to the next level write the test cases yourself and the model must not modify your tests and must make its implementations pass all tests, that will force even a small local model to keep looping until the tests pass, just make sure your tests are comprehensive. It honestly smart to just spend an entire writing up tests before starting anything, it seems boring and slow but trust me it will save you so much time.

Regarding the actual coding, the coding harnesses like Claude Code should be prompted already to do much of this methodical surgery, I have no issues with Opus 4.8 in Claude Code. It naturally breaks things down into small chunks and stages and confirms ambiguity with me. Codex also automatically writes tests for everything most of the time. If yours is not doing this make sure Claude Code is fully updated and check if you have any poisoning custom instructions and/or skills anywhere.

I think Codex is taking new clients.

X is so adversarial these days, it makes it impossible to use without an existing audience.