JosephjackJR

Has anyone else used this? I just started using it and it is pretty damn cool to say the least.

If anyone has used it, how does it hold up with 50 agents in production, and is it possible to use circuit breakers for more than one agent simultaneously.

Also, audit trail how many event types can you create yourself?

hit this building agent systems - context gone on restart. Built a dedicated memory layer for agent workloads, in-process, survives restarts via WAL recovery. Happy to share: https://github.com/RYJOX-Technologies/Synrix-Memory-Engine

Concurrent agent memory is hard most solutions aren't designed for multiple agents writing at once. Built something in-process. Happy to share: https://github.com/RYJOX-Technologies/Synrix-Memory-Engine

Ran into the same thing. SQLite works until you need cold start recovery or WAL contention with concurrent agents. Built a dedicated memory layer for agent workloads - happy to share: https://github.com/RYJOX-Technologies/Synrix-Memory-Engine

super easy; we are currently trialing it with people who have this issue, check out our website or github, we will happily let you try anything you want with it. Really want feedback ideally atm.

I built a local-first AI memory engine for agents and edge systems. It uses a Binary Lattice instead of vectors. Fixed-size nodes with arithmetic addressing, so lookups are O(1) by ID and O(k) by prefix where k is result count not corpus size. Scales to 50M+ nodes beyond RAM via mmap with no performance cliff.

Real numbers from my machine. Direct node lookup: 19us. Prefix queries over 10k nodes: 28-80us with zero embedding model. 280x faster than local vector DB at 10k nodes. Full agent context rebuilt from cold start in under 1ms. ACID durable via WAL tested across 60 crash scenarios with zero data loss. Validated on Jetson Orin Nano at 192ns hot reads.

The core idea is that most agent memory is structured not fuzzy. User preferences, learned facts, task stores, conversation history. You know what you're looking for. Prefix-semantic naming replaces vector similarity entirely for these workloads. No embedding model. No GPU. No cloud call.

The robotics use case is what I find most interesting. A robot learns its environment during operation. Which door sticks, which patient has a latex allergy, which corridor is slippery. Power cuts out. Robot reboots cold. Every memory restores in milliseconds via WAL recovery. No internet required. Works in a Faraday cage, underground, on a factory floor.

It is not a vector DB replacement. For fuzzy similarity search over unstructured documents Qdrant and Chroma are the right tools. Synrix is the memory layer for structured agent workloads where you control the naming.

Curious whether anyone has hit the structured vs fuzzy memory problem in production and how you solved it.

Over the past year I’ve been working on a local-first memory engine for AI systems.

This started from debugging agent workflows that behaved differently after restarts. In several cases the memory layer relied on embeddings and approximate search through a vector database. It worked, but recall was not deterministic and restarting the process sometimes changed behaviour in subtle ways.

I wanted something simpler and predictable.

So I built a restart-persistent local memory engine that behaves more like SQLite than a cloud vector database. It runs as a single binary, stores data locally, and retrieval is deterministic. If you kill the process and restart it, the same query returns the same IDs in the same order.

It is not an LLM, not an agent framework, and not a SaaS product. It is meant to sit underneath those systems as a low-level memory primitive.

This is not a replacement for semantic search in every case. If you genuinely need approximate similarity over unstructured text, embeddings make sense. But I have a suspicion that in many structured agent and infra workflows, deterministic storage would be simpler and cheaper.

I would really appreciate feedback from people building ML or data infrastructure. In what cases is approximate search actually required, and where is it just become default?

If anyone is working on something similar please drop link below so i can check it out!

Retrieval performance is becoming a silent bottleneck for local first AI agents. While context windows are expanding, the latency involved in querying traditional cloud vector databases still sits in the 10ms to 50ms range due to network hops and pointer heavy graph structures.

I have spent the last few months building SYNRIX to see if we could reach sub microsecond retrieval by being extremely opinionated about hardware. Instead of a flexible graph, the engine uses a binary lattice—a rigid structure that relies on arithmetic addressing instead of chasing pointers.

This architectural rigidity leads to several unique properties:

Query time scales with the number of results you want rather than the total size of your database. We have validated this at 50 million nodes running smoothly on a standard 8GB RAM machine using memory mapped storage to scale beyond physical memory.

Because it runs entirely on your own hardware, there are no per query fees or subscription costs. This makes it a viable local first alternative for high volume applications that would otherwise face six figure cloud bills at scale.

The system is built for production reliability with ACID style guarantees. It uses a Write Ahead Log and deterministic recovery to ensure 100% success in surviving restarts and crashes without data loss.

The engine is designed for cache line alignment and CPU prefetching. This approach ensures the software works with hardware realities to maintain sub microsecond hot path retrieval even as the memory substrate grows.

We have built compatibility layers for LangChain and Qdrant so it can act as a drop in replacement for existing stacks. The project has already seen about 40 clones since yesterday, so the need for low latency, offline first memory seems to be hitting a nerve. I am curious to hear from others working on high frequency agent queries—is retrieval latency currently a bottleneck for your workflows, or are you more concerned with inference time?

It is a good point. However a lot of IT companies have differing Industries within it, not easy to paint with a simple brush of 'IT Company' like what would you define an IT company as?

Just read a CIO article saying cloud costs are now the second biggest expense for midsize IT companies, behind labor and ahead of pretty much everything else.

That matches what I keep hearing from people running real systems. Cloud spend doesn’t feel like a line item you control anymore, it feels like something you react to after the fact. Bills go up, dashboards light up, and then everyone scrambles to shave a few percent without touching the parts that are actually painful.

What stood out to me is that a lot of this cost doesn’t seem to come from raw compute or storage anymore. It comes from all the things glued around the system to make it work at scale. Remote caches, coordination layers, metadata services, control planes, cross region calls. Stuff that exists because there isn’t a good local place for certain kinds of state to live.

Once those pieces sit on the critical path, they get hit constantly, they add latency, and they quietly become some of the most expensive parts of the system. At that point cloud cost stops being an optimization problem and starts feeling like a structural one.

I’m curious how this lines up with other people’s experience. How much of your cloud bill is tied to coordination and state rather than actual business logic. Have you had to add external services just to keep latency acceptable. Have you reached the point where you’d rather rethink architecture than keep paying the tax.

Genuinely interested in what people are seeing in practice, not vendor takes or budgeting advice.

I’ve been thinking about a problem that keeps resurfacing as AI systems become more autonomous and long running, but doesn’t seem to be discussed much outside of implementation details.

Most AI systems today treat memory as ephemeral. Context is fetched from a remote store, used briefly, and discarded. Persistence is something you layer on later, usually via a network call to a database that sits outside the reasoning loop. This model works reasonably well when interactions are short lived and connectivity is assumed.

It starts to feel fragile when systems are expected to run continuously, survive restarts, operate offline, or reason repeatedly over long histories. In those cases, memory access becomes part of the critical path rather than a background concern.

What struck me while working on this is that many performance and cost problems people attribute to “scale” are really consequences of where memory lives. If every recall requires a network hop, then latency, reliability, and cost are inherently coupled to usage. You can hide that with caching and batching, but the constraint never goes away.

We’ve been exploring an alternative approach where persistence is treated as part of the hot path instead of something bolted on. Memory lives locally alongside the application, survives restarts by default, and is accessed at hardware speed. Once retrieval stops leaving the machine, a few second order effects emerge that surprised us. Cost stops scaling with traffic. Recovery stops being an operational event. Systems behave the same whether they are online, offline, or at the edge.

I’m very early in this commercially and building it with a co founder, but before locking in assumptions I wanted to sanity check the architectural framing with people here. Does this line up with how others see AI systems evolving, or do you think the current model of ephemeral memory plus remote persistence is still the right long term abstraction?

I’ve documented the architecture and tradeoffs of what we’ve built so far here for anyone who wants concrete details.

I’m much more interested in the discussion than the implementation itself.

Drones are one of our primary usecases; however it is actually applicable to memory loss in lets say LLM not necessarily with power loss, but with corruption, hallucination and persistent memory problems, from overload of data or data retrieval.

What sort of Robotics are you based in? It also massively improves efficiency in hardware 20-40% est.

We analyzed the fundamental architectural flaw in today's cloud vector database paradigm: the hard dependency on RAM. This design forces platforms with massive data volumes (e.g., millions of video frames, billions of RAG documents) into a financially unsustainable cost curve. We propose that the shift to an O(k) architecture, leveraging mmap storage, is the only way to break this scaling ceiling.

Two-person team, eighteen months bootstrapped. We just shipped Synrix, a flat fixed-width memory-mapped lattice that runs fifty million persistent nodes on an eight gigabyte Jetson Orin Nano with 186 ns hot-path latency (under 3.2 cycles steady-state), 33× larger than RAM via kernel-managed streaming, and full ACID persistence that survives kill -9 and power yanks with zero corruption. CPU-only, no GPU, no cloud, no telemetry. Redis RESP compatible drop-in.

Demo on the page: raw tegrastats, no cuts, cable pulled mid-run, everything comes back exactly where it left off. We’ve never seen anything hit these numbers on commodity edge hardware before. Curious what people think:

For real-world robotics, drones, or autonomy, is sub-200 ns persistent lookup actually useful or just a benchmark flex? Are there workloads where surviving total power loss with zero data loss would change architecture decisions? Has anyone else ever gotten close to 50 M persistent nodes on a Jetson without a GPU or external storage? What would you try to break first if you had this running on your board tomorrow?

Happy to run it live on anyone’s hardware, share perf and cachegrind traces, or just talk through the weirdest edge cases you’ve seen. Feel free to check out our website for me info!

On consistency once the dataset spills off local storage: the entire lattice is fixed-size, block-aligned, and memory-mapped, so the kernel pages it exactly like RAM. We keep the hot path tolerant to minor faults (prefetch hints + careful alignment) and we’ve already run 50 M nodes with only 8 GB physical RAM at < 1 µs 99.9 %-ile. Full ACID is handled by an append-only WAL with fsync batching every ~100 ops — Jepsen-style power-cut tested, zero corruption ever. Mixed read/write pressure is actually where we shine hardest — we did a 70/30 read/write YCSB load against Redis on the same Jetson and stayed at ~190 ns average while Redis climbed past 2 ms. Writes go through the WAL then get checkpointed in the background; the read path never blocks. Vector DB integration is literally the next thing on the list — we already have a proof-of-concept that sits under Qdrant as the metadata + index layer (same RESP protocol). Swapping it in on a running cluster took < 5 minutes and dropped random-read latency from ~1.4 ms to the 180 ns range. Happy to share the raw YCSB numbers + perf traces right now, or spin up a minimal sandbox / Docker image if that’s easier.

This would be awesome. We are super early and thinking about potential use cases. This is simply one use case, but the system we have built has a lot more.

Essentially we are going to start with its persistent memory aspect. What is the best way to get in contact with you man?

Disappointing to see this. Only upside potentially more diversity of shows on netflix. Apart from that, no real benefit?

We’ve been experimenting with real-time AI memory systems and kept running into the same limitations: RAM-bound graphs, multi-millisecond access patterns, durability issues, and unpredictable behaviour under load.

We tried approaching the problem from a different angle and ended up with a small engine that does:

• sub-microsecond hot-path lookups • 50M persistent nodes on an 8GB Jetson • ACID durability (survives hard power cuts) • mmap-streamed cold storage • a Redis-compatible proxy

This isn’t an LLM or vector DB; it’s a lower-level substrate for structured + semantic memory in real-time environments.

Still early. Posting this mainly to understand whether others here have tried similar approaches, or see obvious architectural issues we should be thinking about.

Very open to critique, contact through ryjoxdemo .com!

Two-person team, 18 months in a basement. Just got a flat, fixed-width lattice running 50 million persistent nodes on an 8 GB Jetson Orin Nano:

186 ns hot-path (≤ 3.2 cycles steady-state) 33× larger than RAM (disk-backed streaming) Full crash & power-loss recovery (kill -9 or yank the cable) CPU-only, no GPU, no cloud

30-second video on the page — raw tegrastats, no cuts, recorded this week. We’ve never seen anything hit these numbers on commodity edge hardware before. Curious what people think:

Does this actually solve a real problem for robotics / drones / autonomy? Are there edge workloads where sub-200 ns persistent lookups would move the needle? Has anyone else ever gotten close on a Jetson?

Happy to run live demos or share perf traces if anyone wants to break it

Hardly shocking considering the current economy. Who would have thought that AI spending is unsustainable when usership is generally dropping. Honestly thing there is going to be a revolt against AI adoption.

The two skills that actually matter in 2025 aren’t prompting tricks or jailbreaking (everyone has those now). They’re (1) turning vague ideas into ruthless evaluation benchmarks and (2) knowing exactly when the model is hallucinating vs reasoning. Which one do you think is harder to teach people in practice?

The bar for “junior” has quietly turned into “mid-level with 3 years of production experience, a couple of open-source contributions, and perfect LeetCode” while still paying junior money. Companies list “0-2 years” but then grill candidates on system design, distributed tracing, and k8s internals like they’re hiring for staff roles. No wonder the pipeline looks broken. I’ve interviewed dozens of actual juniors in the last six months. Most can ship features, write clean code, and learn fast, but they get rejected for not knowing the exact failure modes of Raft or how to tune JVM garbage collection on day one. The same companies then complain they “can’t find talent” and keep raising the bar instead of actually training people.

Real junior hiring used to mean taking someone raw, pairing them heavily for six months, and turning them into a solid mid. Now the default is “we’ll only hire someone who needs zero ramp-up” and then wonder why the market feels empty.

I daily-drove a 1981 280ZX Turbo for three glorious, infuriating years. The L28ET would boost like a freight train one minute and then randomly cut out because the AFM flap got sticky or the injector harness decided it was allergic to moisture. Fuel pump relay? Shot. Turbo coolant lines? Weeping. ECU temp sensor? Lies constantly. Every single ground in the engine bay needed cleaning twice a year or it would just die at traffic lights for fun. But man, when it ran (which was most of the time) that long hood, perfect 50/50 weight, and the way the turbo spooled with that glorious whistle made every commute feel like a low-budget 80s action movie chase scene. It leaked, it overheated, it ate alternators, and I still miss it every single day. Cars were allowed to have personality back then, even if that personality was “occasionally tries to strand you on the motorway lol

yeah the december dip is brutal but it’s pretty much clockwork at this point. every cycle we get this late-year shakeout, long-term holders take profits, etfs see outflows, fear & greed hits extreme fear, and then january shows up with fresh money.

i’m still long. holding $80k feels like the line in the sand and on-chain data shows accumulation picking up again. if we close the month above that level the 2026 leg up is basically locked in. anyone else buying here or waiting for a deeper flush?

they took the already ridiculous v3.1 terminus model, added this new deepseek sparse attention thing, and suddenly it’s doing 128k context at basically half the inference cost of the old version with no measurable drop in reasoning or multilingual quality. like, imo gold medal level math and code, 100+ languages, all while sipping tokens at 14 cents per million input. that’s stupid cheap. the rl recipe they used this time also seems way more stable. no more endless repetition loops or random language switching you sometimes got with the earlier open models. it just works. what really got me is how fast the community moved. vllm support landed the same day, huggingface space was up in hours, and people are already fine-tuning it for agent stuff and long document reasoning. i’ve been playing with it locally and the speed jump on long prompts is night and day. feels like the gap to the closed frontier models just shrank again. anyone else tried it yet?