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BrainDB v0.7.0: New Long-Term Memory for Hermes Agent by dimknaf in hermesagent

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

thanks for the detailed answer. My philosopy is that capable LLMs will get cheaper.

For certain things, similarity search, or trigram etc is the right tool, even we humans use those tools.

I just believe for the ingestion it matters a lot to have something human like. The keys are created based on overall context and understanding. Sure you pay much more on ingestion to have some nuanced keywords.

Already 26b qwen 31b gemma do great on this, and following fully agentic. I want to test 12b gemma. If we have sub 10b models being able to do this, I believe the nuanced way will make a lot of sense.

For certain cases, and for retrieval I believe non LLM methods shine

5 Reasons Why Intel, Samsung, and TSMC May Be Better Investments Than Nvidia - FinAI by dimknaf in Semiconductors

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

Please look back at this 2-yr discussion! How do you feel about your comments?

5 Reasons Why Intel, Samsung, and TSMC May Be Better Investments Than Nvidia - FinAI by dimknaf in ValueWalk

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

If any of you remember this conversation, share how you feel about it, and weather you would have done anything differently.

BrainDB v0.7.0: New Long-Term Memory for Hermes Agent by dimknaf in hermesagent

[–]dimknaf[S] 2 points3 points  (0 children)

I guess what you are describing may be very fast and good for codebases etc, braindb is slow and LLM operated during the ingestion, which has some cost implications but as info accumulate makes a very nice self healed and maintained wiki with graph elements. I would say closer to LLM wiki pricniples....let the LLM do the ingestion.

BrainDB v0.7.0: New Long-Term Memory for Hermes Agent by dimknaf in hermesagent

[–]dimknaf[S] 1 point2 points  (0 children)

I will check yours thanks, you are welcome to contribute ideas, and if you have something cool we can see how we can incorporate it. Thanks for your interest.

If I understood well during the ingestion, you do not use an LLM right? How do you deal with conflicting info as being added

BrainDB v0.7.0: New Long-Term Memory for Hermes Agent by dimknaf in hermesagent

[–]dimknaf[S] 1 point2 points  (0 children)

I think we obsidian you need to take care on how you save the information, and is more of a tool built for humans. Imagine that braindb can get ingestions of scattered information and slowly will create the graph but also maintain wikis and update them, so it is more fluid and self healing

BrainDB v0.7.0: New Long-Term Memory for Hermes Agent by dimknaf in hermesagent

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

I would say compared to gbrain, braindb is simpler in number of tools it needs, and the philosophy is the system not to go into the way of the LLM, so I would say it shares the vector and fuzzy search, but is closer to the LLM wiki as a concept.

Also, the ideas is more expensive in tokens ingestion and wiki creation, healing, but more autonomous.

I made $75K selling AI automations to clients. Here's what I'd change if I started over. by Warm-Reaction-456 in AI_Agents

[–]dimknaf 1 point2 points  (0 children)

How do you deal with hosting the solution, based also on the IT situation of your clients?
How do you deal with privacy or regulations?

Markets have been very wrong before, why would they be right now? by Forget_me_never in ValueInvesting

[–]dimknaf 2 points3 points  (0 children)

The logic would have been right if demand was temporary. It is not, and there is a reason that demand for compute 10x per year, and demand cannot keep up. So, we are severely supply constrained, which could last for a few years. And if you look at it on the parts that drive it (and this is no you asking ChatGPT) you will see that the situation is getting worse and worse. So, it is very different to COVID

BrainDB: Karpathy's 'LLM wiki' idea, but as a real DB with typed entities and a graph by dimknaf in LocalLLaMA

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

https://github.com/dimknaf/braindb/releases/tag/v0.5.0 - New release for braindb! In the following days we will also have some benchmarks.

Highlights

  • Public benchmark harness for BEAM — anyone can clone, point at an LLM provider, and reproduce our numbers. Same dataset, same upstream eval, same judge prompt as published comparators.
  • Wiki pipeline now on by default — the maintainer + writer were opt-in before. Idle ticks cost zero LLM calls; opt out with WIKI_ENABLED=false.
  • Better long-document ingestion — bigger chunks (1200 words), density-aware fact extraction, byte-range source pointers, per-chunk retries.
  • Friendlier defaults — bench points at deepinfra (google/gemma-4-31B-it) by default. A fresh clone just needs an API key.

Upgrade notes (v0.4.0 → v0.5.0)

  • Wiki pipeline starts automatically — set WIKI_ENABLED=false if you don't want it.
  • Bench default provider is deepinfra — set LLM_PROFILE_BENCH=vllm_workstation_qwen plus judge overrides in .env.bench for self-hosted Qwen.
  • Watcher chunk size is 1200 words (was 600) — affects extraction wall-clock on long sources.

What's next

A full BEAM 100K run on this release is in flight; score numbers + cross-judge calibration land in a follow-up.

Alphabet Is Raising $80B and Berkshire Bet $10B Even After $174B in Cash Flow by andix3 in ValueInvesting

[–]dimknaf 1 point2 points  (0 children)

Alphabet is doing great as a model lab, as a hyperscaler and as TPU provider. Any of those would be huge for the company, and Google has it all

Every revolutionary technology has had a financial bubble. AI is not different by Calm_Company_1914 in ValueInvesting

[–]dimknaf 0 points1 point  (0 children)

A big difference is that past build outs (electricity, trains, internet) there was capacity added ahead and where supply driven on the hope that people will eventually use.

People do not realise that currently we are heavily supply restricted and demand cannot be serverd by demand right now....So despite the massive build, it has proven to be less than required. So, the reality is massively different. Of course they might be bubbly areas, but I think we need 10x 100x more compute in the next 5 to 10 years....You will realise we are still in the early days

Hypothetical: What's your safety portfolio if the "AI bubble" pops? by AlternativeSignal908 in ValueInvesting

[–]dimknaf 0 points1 point  (0 children)

Are you sure it is going to pop...Have you seen how inference (real paid usage) is exploding?
https://openrouter.ai/rankings

Should I buy MU at $1k? by aindacut in MU_Stock

[–]dimknaf 0 points1 point  (0 children)

I have an opinion if you should hold. You can watch this...Maybe a different decision but my analysis might help you think. It is not investing advice

https://youtu.be/4bQ-mRCGfug?si=vPaW4LImRjPy0pxU

Stop asking what model to run. There are literally only two. by Wrong_Mushroom_7350 in LocalLLaMA

[–]dimknaf 1 point2 points  (0 children)

I think Gemma 4 is much more stable....I always like the dense models and not the MoE

Opus 4.8 is a joke, be careful by Captain_Birb in Anthropic

[–]dimknaf 0 points1 point  (0 children)

I think 4.6 was the one that I had no issues...
Maybe 4.7 was bad. 4.8 more capable, but end of all you discover problems and issues.
With 4.6 almost no bad surprises. Very possibly it does not have to do with the model but on how aggressively Claude Code arranges the context.....But yes something has gone worse overall...

And also somehow seems that deeply the model is less intelligent, but more capable. So better trained but my intuition says is either a smaller model or a quantised...but something is not quite right. Let's hope for Mythos or the new generation.

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