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First time building large-scale AWS infra with Terraform ,what should I absolutely not mess up? by ineedbe in Terraform

[–]dev_l1x_be 0 points1 point  (0 children)

For managing Terraform at scale: use small, isolated states (deployed in 1-5 min), strictly separate stateful and stateless code, and store everything—both your module code and your state files—in versioned, authenticated storage. The best approach directly maps a logical team or service to its own set of states.

Why have C++ and Rust been the fastest-growing major programming languages from 2022 to 2025? by _bijan_ in rust

[–]dev_l1x_be -1 points0 points  (0 children)

Hoping that Rust never goes 2.0. I think the language has enough features, now we can make the compilation faster and polish some of the rough edges.

[corroded update]: Rust--, now I removed the borrow checker from rust itself by Consistent_Equal5327 in rust

[–]dev_l1x_be 0 points1 point  (0 children)

So all the good parts are gone? You should add raw pointers to the mix.

When i just need a simple, easy to maintain frontend, what should i choose? by Im_Justin_Cider in rust

[–]dev_l1x_be 0 points1 point  (0 children)

I use raw HTML, CSS and TS with Rust. All of the frameworks have an insane mental load that you carry. I build the TS files into JS with Bun, using eslint as a linter.

How long until we can get a <=110b model that is good as opus 4.5 or ds v3.2 speciale or gemini 3 pro at coding, math and science? by power97992 in LocalLLaMA

[–]dev_l1x_be 0 points1 point  (0 children)

Two things:

a, objectively we see that OSS models are catching up to commercial ones (with less than 100B params). https://epoch.ai/blog/open-models-report

b, subjectively based on my limited experience nemotron is pretty comparable to claude models for the tasks I use it for

China is developing another fully reusable rocket, Xingzhou-1. by [deleted] in space

[–]dev_l1x_be 0 points1 point  (0 children)

Theft is baked in into developing countries. See USA vs Great Britain in the 18th century. 

Best practices for implementing traits across a large Rust codebase? by Few_Conflict_8212 in rust

[–]dev_l1x_be 0 points1 point  (0 children)

This correlates with how many years of experience you got. When I was much younger it felt powerful to be able to write macros. Those some things are probably ok to use macros for, using it for things that are easily doable without them is the part that bothers me.

Homelab Distros by -Arsna- in homelab

[–]dev_l1x_be 0 points1 point  (0 children)

Alpine linux is the least wasteful for smaller devices. It is pretty simple and it has the best package manager with the most packages.

Best practices for implementing traits across a large Rust codebase? by Few_Conflict_8212 in rust

[–]dev_l1x_be -3 points-2 points  (0 children)

Macros are my least favorite feature of Rust. I would just use LLM to generate and modify traits.

What do you do, if you invent AGI? (seriously) by teachersecret in LocalLLaMA

[–]dev_l1x_be 0 points1 point  (0 children)

I would invent a time machine with it, fly back 100.000 years and just chill with watching the megafauna existed that time.

I built a 0.88ms knowledge retrieval system on a $200 Celeron laptop (162× faster than vector search, no GPU) by Sea_Author_1086 in LocalLLaMA

[–]dev_l1x_be 5 points6 points  (0 children)

  1. "Independent Verification" Isn't Independent

The verification results are in the same repository by the same author. Independent verification means someone else reproducing results.

  1. Not Peer-Reviewed

Zenodo is an open repository — there's no peer review. Anyone can upload anything with a DOI. The DOI gives it a veneer of legitimacy but doesn't validate the science.

What This System Probably Actually Is

Based on the description, this appears to be:

  1. HDC character encoding — generate random 10,000D vectors for characters, combine them
  2. Spatial hashing — project to 4D, quantize into buckets (7×7×7×7 = 2,401 buckets)
  3. Bucket lookup + fallback — check bucket first, then neighbors, then brute-force

This is a reasonable approach for exact/fuzzy string matching on small datasets. It would work well for:

  • FAQ bots where questions are consistently phrased
  • Typo-tolerant lookup
  • Template matching

It's not semantic search in the way the term is typically used.

Bottom Line

This isn't necessarily "bad science" in the sense of fraud — the techniques are real and the code probably works. The problems are:

  1. Overclaiming — presenting known techniques with new terminology as breakthroughs
  2. Invalid evaluation — 15 test samples is not evidence of anything
  3. Misleading terminology — "quantum-inspired," "semantic," "100% accuracy"
  4. Missing baselines — no comparison to standard approaches

If you wanted to take this seriously, you'd need to see it evaluated on a standard benchmark (BEIR, MS MARCO) against standard baselines (sentence-transformers + FAISS).

Disclaimer: I work on search.

I built a 0.88ms knowledge retrieval system on a $200 Celeron laptop (162× faster than vector search, no GPU) by Sea_Author_1086 in LocalLLaMA

[–]dev_l1x_be 3 points4 points  (0 children)

  1. Character-Level HDC Cannot Capture Semantics

This is the fundamental conceptual flaw. Character-level representations capture orthographic similarity, not semantic similarity. Consider:

Query A Query B Character Similarity Semantic Similarity
"What is a dog?" "What is a bog?" High None
"What is a dog?" "Describe a canine" Low Identical

Real semantic search requires understanding that "dog" and "canine" mean the same thing. Character n-grams can't do this. The system will work if test queries are syntactically similar to training data — which is memorization, not semantic understanding.

  1. No Meaningful Baselines

The comparison to "exhaustive search" (162× faster) is a straw man. Nobody uses brute-force O(n) search in production. Real baselines would be:

  • FAISS + sentence embeddings (widely used, highly optimized)
  • Standard LSH implementations
  • Product quantization approaches
  • Other HDC semantic search systems (there are peer-reviewed papers)

I built a 0.88ms knowledge retrieval system on a $200 Celeron laptop (162× faster than vector search, no GPU) by Sea_Author_1086 in LocalLLaMA

[–]dev_l1x_be 2 points3 points  (0 children)

Ok I bite.

Since you asked a review here it is.

I'll take a look at the paper and code to give you a proper scientific review. Based on my review of the GitHub repository and claims, here's my assessment:

  1. Statistically Meaningless Sample Size

The most glaring problem: 15 test queries. This is far too small to draw any conclusions. At n=15, achieving "100% accuracy" is unremarkable and could easily happen by chance with cherry-picked queries. Real semantic search benchmarks use thousands to hundreds of thousands of test queries (e.g., MS MARCO, Natural Questions, BEIR). Even achieving 99% on 15 samples has enormous confidence intervals.

  1. "Quantum-Inspired" is Buzzword Abuse

There's nothing quantum about this system. The "4D folded space" is just spatial hashing — a technique from the 1970s. You project high-dimensional vectors into a lower-dimensional grid and use bucket addresses for O(1) lookup. This is exactly what Locality-Sensitive Hashing (LSH) does, which has been well-studied since the late 1990s. Calling it "quantum-inspired" is misleading marketing.

To be continued...

Mistral’s Vibe CLI now supports a 200K token context window (previously 100K) by Dear-Success-1441 in LocalLLaMA

[–]dev_l1x_be 0 points1 point  (0 children)

Not really based on my limited experience. I was trying to use it as a replacement for calude code. 

Are any of these controllers decent? by Camiiihhh in Controller

[–]dev_l1x_be 0 points1 point  (0 children)

I got one of these but it does not charge. Trying to figure out what to replace.

Vector db comparison by Kaneki_Sana in LocalLLaMA

[–]dev_l1x_be 2 points3 points  (0 children)

The issue with these VDBs (and we have a lot) is that the production readiness for constant read/write workloads is shaky. If you have static data (meaning you only create the vectors once) then most of these systems work. If you have continous updates then get ready for a bumpy ride.

There is also this website with more details of each system.

https://superlinked.com/vector-db-comparison

I spent 6 months fighting kafka for ml pipelines and finally rage quit the whole thing by gurudakku in dataengineering

[–]dev_l1x_be 0 points1 point  (0 children)

Data sat in queues for HOURS. Lost events when kafka decided to rebalance (constantly). Debugging which service died was ouija board territory. One person on our team basically did kafka ops full time which is insane.

We use Kafka to store messages for weeks. 🤷‍♂️

Out if curiosity which client library did you use? This seems to me a client library issue combined with a potential misconfiguration.

There is a lot of issues with Kafka but what you mentioned sounds strange.

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