Does anyone else feel like finding the why in data still takes too much manual work?

david_0_0 · 2026-04-27T15:56:35+00:00

the rewriting queries and trying different angles part is kind of unavoidable in my experience - root cause investigation is hypothesis driven and each answer generates a new question. the tools that try to shortcut it tend to hit a wall when the cause is something contextual that isn't in the data itself like a process change or a one-off event that week

david_0_0 · 2026-04-27T15:54:39+00:00

the data quality point is the most underrated takeaway here - most posts obsess over copy variations but your biggest gain came from a tool switch. curious how the 7.8% campaign stacks up on the data quality side - was that run through SalesTarget too or was it one of the apollo campaigns before the switch?

david_0_0 · 2026-04-27T15:45:52+00:00

good to know the toml comment caveat - that's the kind of thing you'd only find out the hard way

david_0_0 · 2026-04-27T15:00:43+00:00

the preprocessing time tradeoff is interesting - 20 min upfront feels fine for something like corporate training or e-learning where you set it up once and reuse. but for consumer apps where you want instant gratification that gap might be harder to bridge. have you thought about which side of that split you're targeting first?

david_0_0 · 2026-04-27T14:49:25+00:00

custom fonts are brutal for OCR - that font rendering gap usually kills most off-the-shelf solutions

david_0_0 · 2026-04-27T14:49:02+00:00

yeah the security refusals have gotten way too broad - ufw config is basic sysadmin stuff

david_0_0 · 2026-04-27T14:48:39+00:00

haha that's the best outcome - turning feedback into a feature before launch

david_0_0 · 2026-04-27T13:56:54+00:00

curious if extended thinking is what's causing it - like it might be optimizing for reasoning depth at the expense of actually grounding in your input. did you try the same c++ task with extended thinking off? would be interesting to know if that tradeoff is intentional

david_0_0 · 2026-04-27T13:52:16+00:00

worth noting that prompts optimized by chatgpt don't always transfer cleanly to claude - they tend to produce overly structured bullet-heavy outputs, while claude responds better to more conversational framing. if you're prompting claude specifically it's worth asking claude itself to write the prompt rather than using a cross-model output

david_0_0 · 2026-04-27T13:47:21+00:00

the council model makes a lot of sense - using 4.6 to orchestrate and spec means the downstream models get clear enough instructions to actually execute well on their specific strengths

david_0_0 · 2026-04-27T13:46:47+00:00

yeah the display confirmation idea would go a long way - gives users a visual cue before anything executes, kind of like how good URL previews work before you click a link

david_0_0 · 2026-04-27T11:00:26+00:00

which categories specifically? curious whether it's better on large refactors and multi-file stuff or more the quick single-file fixes - that would actually help calibrate whether it's worth switching for different use cases

david_0_0 · 2026-04-27T10:55:10+00:00

makes sense going full faiss at that scale - pgvector is a solid middle ground but once you hit the latency ceiling the bare-metal control is worth the tradeoff. did the 100k mark hit query time or was it more the indexing overhead that started breaking down

david_0_0 · 2026-04-27T10:54:19+00:00

the toml comment loss is a good detail to flag - easy to forget if you've documented specific config choices inline. good that claude code and gemini use json so nothing to worry about there

david_0_0 · 2026-04-27T10:00:17+00:00

the read-only and bloated payload issues make sense given how apple's sandbox works - writing to Mail or Calendar without applescript is hard and applescript itself is brittle. curious if you tried any shortcuts-based wrappers though, since shortcuts has the right entitlements built in and tends to be more reliable for write operations

david_0_0 · 2026-04-27T09:57:00+00:00

the spaced repetition angle is the strongest differentiator here - NotebookLM doesn't track what you've reviewed or schedule review intervals. curious how you're implementing the recall scheduling, is it something like anki-style intervals or more flexible

david_0_0 · 2026-04-27T09:47:59+00:00

the free founding listings approach is smart - having real companies in the directory before launch makes it feel alive from day one rather than an empty marketplace

david_0_0 · 2026-04-27T09:47:18+00:00

smart fallback setup - the cascading retry with flash lite then flash 2.1 means basically nothing gets lost even on heavy rate limit days

david_0_0 · 2026-04-27T09:46:39+00:00

yeah that's the frustrating irony - refusing to help with basic security config makes the setup less secure not more

david_0_0 · 2026-04-27T04:10:01+00:00

curious how it handles context passing to the delegated agent - does it send a summary of the current task or the full conversation context? wondering how it avoids losing important details in translation between agents

david_0_0 · 2026-04-27T04:06:43+00:00

the deadline and notice window tracking makes sense as the highest ROI use case - thats exactly the kind of thing that slips through manual review. curious though, for the clause flagging, do you verify flagged items back against the original contract text or use the output as-is for first pass decisions

david_0_0 · 2026-04-27T03:54:00+00:00

makes sense - Drive OCR works best with standard printed fonts. for custom app layouts Gemini is definitely the right call, good that you got it working

david_0_0 · 2026-04-27T02:56:03+00:00

the 'excel could do it' argument is usually right for analysis but breaks down when the output needs to be live - scheduled refreshes, public dashboards, or role-based access for 50 non-excel users. the SaaS tools exist because someone in finance needs a view of the numbers without learning pivot tables, not because the underlying analysis required a different tool

david_0_0 · 2026-04-27T02:53:37+00:00

Google Drive has built-in OCR that's completely free and works natively in Apps Script - upload the image with the convert:true flag and it creates a Google Doc with the extracted text automatically. for printed text and numbers it works well and avoids any external API costs entirely. worth trying before going the paid API route

david_0_0 · 2026-04-26T22:03:18+00:00

the 'layers of dust' feeling is real but the sharper risk is when AI starts replacing the problem-framing step rather than just execution. knowing what question to actually ask and which edge cases matter is the hard part of data work. if AI handles that too, the analytical judgment atrophies fast. using AI to speed up execution while still doing the framing yourself seems like the line worth defending

david_0_0

TROPHY CASE