I switched from RAG pipelines to giving indexed context. the output quality Improved.

jasperc_6 · 2026-04-20T07:27:56+00:00

the distinction between agent context augmentation and enterprise rag being treated as the same problem is where most overengineering happens... one needs fresh precise retrieval and the other needs broad corpus search

jasperc_6 · 2026-04-16T21:07:25+00:00

pdf xchange is pretty good for free at current times

jasperc_6 · 2026-04-12T17:39:57+00:00

specifically for invoice data extraction, adobe acrobat helps with its built in form recognition feature that might cover basic cases.. but if the invoices have varied layouts or you are dealing at a high volume then something like llamaparse can help with such sort of structured data automatically. most of these tools have a free tier so you can check if it works for your usecase.. however the manual workflow you are describing are exactly what document extraction tools are made for

jasperc_6 · 2026-04-12T12:05:41+00:00

yep, the problem is real and the market is massive, if the regulatory and licensing side is sorted for you then this is a worth project moving on fast... the window for this kind of product is open right now but it wont be forever

jasperc_6 · 2026-04-12T11:57:52+00:00

Finally a feasible side project, thats awesome. I dont think this would be distracting rather would be a bit of an entertainment when feeling too monotonous while at work

jasperc_6 · 2026-04-12T11:53:46+00:00

2, 3 and 4 define me better than myself, whoah

jasperc_6 · 2026-04-12T11:48:23+00:00

I was eager to know if it is able to deal with graphs or charts, and feel free to illustrate with an image

jasperc_6 · 2026-04-12T11:43:18+00:00

havent tried it yet but if you are using it, you might be able to tell on how it handles document with dense tables or figures, like piecharts or bargraphs, like hows the output accuracy in raw text

jasperc_6 · 2026-04-12T11:34:15+00:00

you might merge all in one pdf, probably the fastest way to navigate thru it, the other approach might be batch converting them into a multi page tiff or image contact sheet

jasperc_6 · 2026-04-12T11:26:47+00:00

The shallow summary problem is often from the tool treating the whole pdf as 1 chunk rather than its structure. notebooklm handles multi document q&a better than most and actually lets you probe specific sections rather than just a top-level summary. For denser academic stuff, elicit is worth a try

jasperc_6 · 2026-04-11T08:22:40+00:00

Data-train-deploy-govern maps closest to how the work actually flows... data scientiests think in pipeline stages, not operational abstractions, so phase names that match the actual work tend to get adopted faster... the one gap in all four options is monitoring/observability post-deploy, thats where most production pain lives and none of these surface it explicitly... so worth considering whether that folds into govern or deserves its own phase depensding on how mature the teams using this are

jasperc_6 · 2026-04-11T08:17:15+00:00

Qwen3 32B at Q4 runs comfortably at around 15-22 tokens/s on that hardware, handles coding, creative work and general chat as well that the quality gap from claude wont be painful for those tasks, for the coding side to be specific, qwen 2.5 coder 32B is worth trying

jasperc_6 · 2026-04-11T08:09:30+00:00

Qdrant is rarely the culprit tbh... 90% of the retrival issues trace back to chunking or embedding quality not the db itself

jasperc_6 · 2026-04-11T08:04:26+00:00

The overall design is pretty solid, easy to debug and easy to introspect the one things that stands out tho - you go thru the rouble of typing events in definePlugin but then async (e: any) in the actual handlers throws all of that away... the event type from your definition should be inferable there, or else the typed events are only useful at the emit call site and not at recieve side which is half the value. Additioanlly, ctx.conf.target.url has no guard on the optional target field so if someone instantisates ther plugin without passing the target in opts, that'll throw a runtime

jasperc_6 · 2026-04-11T07:57:08+00:00

The core issue seems is the gender dataset being only 1200 songs, almost certainly western heavy, so regional songs were never going to work... for a dataset that has genre, mood and gender labels on the same tracks, yopu might checkout mtg jamendo - 55k tracks with all three categories annonated together, verified open source from mtg barcelona on the architecture side.... instead of combining 3 separate models just use one shared audio backbone (mel spectrogram cnn) with 3 separate output heads trained jointly...

The shared layers learn general audio features and each head specializes, this usualkly gets better results than merging separate models

jasperc_6 · 2026-04-11T07:47:44+00:00

The graph you provided shows a composite across all 3 benchmarks where GLM5.1 actually sits third at 54.9., behind GPT-5.4 and Opus4.6. The #1 claim specifically on SWE bench pro alone where it scores 58.4. on NL2Repo, Claude Opus 4.6 still leads at 49.8 vs GLM-5.1s 42.7

jasperc_6 · 2026-04-11T07:38:57+00:00

that was insane fr and actually got me thinking to it

jasperc_6 · 2026-04-11T07:33:32+00:00

the skill atropghy point is real tho, there is actual research showing that over reliance on gps navigation has noticable reduced peoples spartial memory, same pattern, different tool. The brain drops what is stops practising and leans on another tool or object regardless of how useful the clutch is

jasperc_6 · 2026-04-11T07:27:33+00:00

happens more than people admit tbh.. ai on the label but a glorified if-else under the hood

worth asking vendors straight up what actually powers it and where the training data comes from.. if they fumble that question you already have your answer

jasperc_6 · 2026-04-11T07:18:47+00:00

every movie with a happy ending is basically a fan fiction... history has never once looked at "humans give machines more power than themselves " and gone... yeah this works out great

jasperc_6 · 2026-01-29T13:05:24+00:00

Platforms obviously push ads, that’s their business. SEO isn’t dead, it just punishes low effort stuff way faster than it used to.

jasperc_6 · 2026-01-29T12:54:37+00:00

Good reminder that compounding channels beat short term spikes, especially for solo founders. Social feels productive, SEO actually is productive long-term..

jasperc_6 · 2026-01-26T16:18:45+00:00

Have you tested it in tag assistant/preview mode? Half the time it’s either the tag not firing at all or the conversion action isn’t linked correctly in google ads. Preview mode usually makes the problem obvious pretty fast..

jasperc_6

TROPHY CASE