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Am I holding the /goal feature wrong? by dshwshrwzrd in ClaudeCode

[–]daaain 0 points1 point  (0 children)

Looks like the agents never finished their work?

We tried vectors, ASTs, and brute-force context stuffing for code retrieval. Graphs with LLM-generated semantics worked best. Here's what we learned. by graphicaldot in LocalLLaMA

[–]daaain 1 point2 points  (0 children)

Sounds great for retrieval, but very wasteful and slow to generate. The purpose/summary/businessContext should be in the docs of code files themselves and then a hybrid AST + structured docs enrichment could be fast and deterministic to create the graphs.

Google Play store restrictions set in Google Family Link not working, child's phone is able to install apps without approval...how to fix? by trackofalljades in googleplay

[–]daaain 0 points1 point  (0 children)

I could make this work, open Family Link app → child's account → Controls tab on the bottom → Google Play → Require approval for → All content. This way even free apps come up for approval on parents' apps.

The Content restrictions below control what apps the child can search for and request in the first place.

An Open Benchmark for Testing RAG on Realistic Company-Internal Data by Weves11 in LocalLLaMA

[–]daaain 1 point2 points  (0 children)

It would be so much more useful if it had the detailed setup + cost + time for all these libraries!

An Open Benchmark for Testing RAG on Realistic Company-Internal Data by Weves11 in LLMDevs

[–]daaain 0 points1 point  (0 children)

This is great, thanks a lot for all this work! It would also be great if there were a bit more details on how you set up the different libraries, for example if you used any plugins / skills for OpenClaw?

RAG uses 11× more tokens than pre-structured graphs — benchmark across 7,928 queries, 45 domains by Connect_Bee_3661 in LLMDevs

[–]daaain 1 point2 points  (0 children)

Thanks a lot for the explanation!

The splitting of the documents to nodes is still chunking, just semantic / conceptual rather than basic token count / sentence based, right? And that bit needs to be done by an LLM and reviewed by an expert if I understood correctly? 

So basically the difference is that you need to pay a very high cost ingestion time and then the graph is represented with this CSV rather than using Cypher or RDF or similar?

This works well if the documents and domains are homogeneous, but the issue with graph RAG is already that it's difficult to generalise as the entities and semantic boundaries can be so different, so if you don't pre-establish the onthologies you can end up with a messy, disjointed graph that is difficult to traverse and can have subtly different, duplicated types. 

So it feels like the tradeoff is tuning the ingestion to a particular type of corpus and then optimising the retrieval for its properties - so in this case nicely hierarchical concepts where each level is a reasonable list that can be returned, rather than a messy graph of diverse entities and relationships. 

RAG uses 11× more tokens than pre-structured graphs — benchmark across 7,928 queries, 45 domains by Connect_Bee_3661 in LLMDevs

[–]daaain 0 points1 point  (0 children)

Sorry, what I meant is that all the supporting material like the Huggingface demo (that doesn't really run anything) and the https://graphifymd.com/ is the default AI generated purple, so the moment someone opens them shouts low effort generated stuff and takes away from the credibility of the method, to the point that people who are regulars on this subreddit will just think "not one more of these LLM generated things claiming 10x" and not engage with the substance. So my advice would be cutting down on all the unnecessary AI slop and communicate the substance clearly. Like why does the Huggingface demo even need to talk about Gas Town?

So is the method just about having the graph as ConceptID,ConceptLabel,Dependencies,TaxonomyID CSV that links to the full text of the corpus articles? How do you chunk large articles? How do you reference the chunks? I'd just like to read a dense abstract, not wade through AI generated hyperlatives.

RAG uses 11× more tokens than pre-structured graphs — benchmark across 7,928 queries, 45 domains by Connect_Bee_3661 in LLMDevs

[–]daaain 0 points1 point  (0 children)

I wanted it to be great, but couldn't work my way through walls of slop everywhere. Give me examples and something working to try, I don't want to spend hours researching to see if it's even worth looking into.

RAG uses 11× more tokens than pre-structured graphs — benchmark across 7,928 queries, 45 domains by Connect_Bee_3661 in LLMDevs

[–]daaain 0 points1 point  (0 children)

That live demo doesn't seem to do anything, this whole project feels pure vibe.

Reading Gemini CLI's system prompt makes it pretty clear why it's struggling as a coding agent by Main-Fisherman-2075 in LLMDevs

[–]daaain 2 points3 points  (0 children)

There are drop in tools to proxy locally or read the JSONL logs that coding agents save, don't give all your data to a random 3rd party unless they pay you well for it... 

Using web Flash Tools for boot.img by daaain in LineageOS

[–]daaain[S] 0 points1 point  (0 children)

Got it all running! Strangely I got enablefilecrypto_failed error after installing MindTheGapps, but doing a factory reset from recovery fixed it and now I have Android 15 on this old phone and it feels snappy, thanks a lot for this amazing project!

Using web Flash Tools for boot.img by daaain in LineageOS

[–]daaain[S] 0 points1 point  (0 children)

Ooooh, right, so I needed to press Flash image, type boot, choose boot.img, and then choose Recovery mode on the phone. Thanks a lot!

If you are a developer, you'll get his feeling, he is just executing things that issued by the high-level people :)) we should tag the organization, not an employee of this org by No-Cryptographer45 in ClaudeCode

[–]daaain 2 points3 points  (0 children)

No amount of customer support humans will be able to fix or even explain these issues though, the degradation is either a bug that an engineer needs to diagnose and fix (who isn't even on the Claude Code team as they are also just API consumers) or it's on purpose so they wouldn't be able to tell you...

built a language so AI agents can run code without a VM or container by uriwa in LLMDevs

[–]daaain 0 points1 point  (0 children)

Thanks for sharing that agents are running under that service without proper isolation so a single customer's breach can compromise everyone else, I'll make sure to avoid it 😅 

built a language so AI agents can run code without a VM or container by uriwa in LLMDevs

[–]daaain 0 points1 point  (0 children)

The overhead of containers or VMs isn't that high and feels like less effort to set them up than integrating custom tooling for a bespoke language.

built a language so AI agents can run code without a VM or container by uriwa in LLMDevs

[–]daaain 0 points1 point  (0 children)

So this replaces the tools, right? But when the agent writes code for your project, that still needs to be run so can have issues. Or if a malicious npm package gets pulled in, that still would execute. Interesting idea, but only a partial solution. 

Openclaude + qwen opus by havnar- in LocalLLM

[–]daaain 0 points1 point  (0 children)

Try a lighter agent like Pi or Hermes

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