Hacker News Reader

_alphageekon Tell HN: Codex Is Down [fixed]

I remember the days when Stack Overflow would go down. People would actually start seeing each other's faces in the office kitchen.

_alphageekon The Same Crypto Exchange Matching Engine Measured 130,000/s and 106/s

Wrapped findings while I was developing matching engine for crypto exchange. I never had time to summarize and write up these findings before. I hope it’s useful.

_alphageekon Ask HN: Who wants to be hired? (September 2026)

Location: Brazil (UTC-3) Remote: Yes Relocate: No Tech: Rust, Nest, Node.js/TypeScript, React, PostgreSQL, AWS/GCP, Rust

Backend/systems engineer, 18+ years.

Recent: g6k-rs — Rust lattice reduction (LLL/BKZ/sieving, CPU/Metal GPU); Orch8 — Rust durable workflow/AI engine, 2,400+ instances/sec; crypto exchange — matching engine, 4,000+ orders/sec, sub-ms latency.

Looking for: Backend/Systems/Tech Lead — Rust or Node.js, Web3/crypto, AI infrastructure. Full-time or B2B.

CV: https://www.ovasylenko.com | hello@ovasylenko.com

_alphageekon A quick look at zero-knowledge proofs

Amazing article. Will save to explain ZKP to others.

One tiny correction

random.randrange(100) gives 300 possible commitments(3 colors for hundred nonces) After seeing a couple of revealed edges, the verifier can figure out the palette and brute-force all 300 combinations, effectively opening every commitment.

It can be mitigated if we use 128 bits of randomness, e.g. secrets.token_bytes(16).

Also I would use sha256 instead of hash. Python hash is not considered secure as it does not have proper collision resistance.

_alphageekon How to lucid dream: a step-by-step guide that takes your sleep seriously

I have other way to do it. Never had a goal actually. There exists "God helmet experiments". You can actually buy a prototype from the one of the scientists who was working on experiment. Google "Shiva helmet system". After 3-4 sessions(once a week) I found that I can do lucid dreaming with no training. Moreover I can wake up 5 times at night and return back to the same dream without efforts. P.S it not advice or ad or proposals, I share my personal experience.

_alphageekon Show HN: Ballet – Workflow automation that writes integrations against any API

Absolutely. And scale does not matter. I had issues with top tier systems like docusign, tableau etc in beforeGPT era. Many undocumented issues and when you write support usually they only were pushing to buy top tier support package, but never fix anything.

_alphageekon Show HN: g6k-rs – lattice reduction framework on rust: supports CPU,Metal,CUDA

I started it 3 months ago to see if I can optimize existing algorithms and grow it up in the huge framework. Though, I develop it in private repository for 3 months, some APIs are unstable and might be changed, GPU support is hardware-dependent. Current repository is a cut of stable/safe api from the private one.

It includes LLL, BKZ, CVP, enumeration, several sieves, arbitrary-precision arithmetic, and experimental Metal/CUDA paths.

Will be happy for any feedback or review shared.

Please contact me at hello@ovasylenko.com / ovasylenko.com for any reason

_alphageekon Ask HN: Who wants to be hired? (August 2026)

Location: Brazil (UTC-3) Remote: Yes (remote only, US/EUR timezone overlap) Willing to relocate: No Technologies: Node.js/NestJS/TypeScript, React, PostgreSQL, Redis, RabbitMQ, Docker, AWS/GCP, Rust (Tokio, Axum, Tonic, WASM)

Résumé/CV: https://www.ovasylenko.com Email: hello@ovasylenko.com

Backend & systems engineer, 18+ years.

Three recent builds: g6k-rs lattice reduction engine supports CPU/Metal/GPU with all contemporary algorithms: LLL, BKZ, Sieving, DeepLLL, Seysen etc with arbitary precision

Rust — Solo-built Orch8 (orch8.io), a durable workflow orchestration engine. 2,400+ instances/sec, <3ms latency. 7 crates, 3 SDKs (Node/Python/Go), 200+ integrations, built-in LLM tool calling and ReAct agent loops. Node.js/NestJS — Matching engine for a crypto exchange processing 4,000+ orders/sec with sub-millisecond latency. Liquidity bots, candle engine, real-time WebSocket trading.

Looking for: Backend/Fullstack developer/Tech lead (Node.js or Rust), Web3/protocol engineering, or AI infrastructure roles. I care about product ownership and shipping - not process theater.

Open to b2b contracts

_alphageekon Rust project goals: Immobile types and guaranteed destructors

Intresting. Immovability becomes a property of the type (!Move) rather than the place (Pin), and the goal is to eventually deprecate Pin outright rather than paper over it with pin ergonomics. !Forge is what finally unblocks safe scoped spawn: handle that can't be mem::forgeten has a destructor that's guaranteed to run.

_alphageekon Ask HN: Who wants to be hired? (July 2026)

Location: Brazil (UTC-3) Remote: Yes (remote only, US timezone overlap) Willing to relocate: No Technologies: Rust (Tokio, Axum, Tonic, WASM),Node.js/NestJS/TypeScript, React, PostgreSQL, Redis, RabbitMQ, Docker, AWS/GCP, zk-SNARKs Résumé/CV: https://www.ovasylenko.com Email: hello@ovasylenko.com Backend & systems engineer, 18+ years. Two recent builds: Rust — Solo-built Orch8 (orch8.io), a durable workflow orchestration engine. 2,400+ instances/sec, <3ms latency. 7 crates, 3 SDKs (Node/Python/Go), 200+ integrations, built-in LLM tool calling and ReAct agent loops.

Node.js/NestJS — Matching engine for a crypto exchange processing 4,000+ orders/sec with sub-millisecond latency. Liquidity bots, candle engine, real-time WebSocket trading.

Also: zk-SNARK wallet engine at Panther Protocol (cross-chain, UTXO model), micro-frontend migration at PropertyGuru (4 product teams). Led 15 engineers across 3 time zones.

Looking for: Senior/Staff Rust systems, backend architecture (Node.js or Rust), Web3/protocol engineering, or AI infrastructure roles. I care about product ownership and shipping - not process theater.

_alphageekon The new HTTP QUERY method explained

>> QUERY request can be cached

I have a weird feeling. Query body is encrypted by https. So CDN will not be able to cache results. In order to make it work right - whole topology of the internet should be redone. Caching on the backend server will not give any real gains for large scale apps.

_alphageekon Ask HN: Who wants to be hired? (June 2026)

Backend & systems engineer, 18+ years. Two recent builds: Rust — Solo-built Orch8 (orch8.io), a durable workflow orchestration engine. 2,400+ instances/sec, <3ms latency. 7 crates, 3 SDKs (Node/Python/Go), 200+ integrations, built-in LLM tool calling and ReAct agent loops.

Node.js/NestJS — Matching engine for a crypto exchange processing 4,000+ orders/sec with sub-millisecond latency. Liquidity bots, candle engine, real-time WebSocket trading.

Also: zk-SNARK wallet engine at Panther Protocol (cross-chain, UTXO model), micro-frontend migration at PropertyGuru (4 product teams). Led 15 engineers across 3 time zones.

_alphageekon Performance of Rust Language [pdf]

I would have liked to have the checks-off delta plotted across rustc versions - the deck notes this stuff moves non-monotonically, so a trend line would say more than a single-version snapshot.

_alphageekon Was my $48K GPU server worth it?

I ve seen already one question like that in the thread. But I rephrase it slightly sharper. Did you consider renting out you setup to vast.ai and if so, how much money it can generate per month deducting electricity.

Also, sorry for the noob question, is not such server generate enormous amount of heat? You did not use any special cooling system?

_alphageekon Apparently Google hates us now

I still have 42k page indexed, but previously I had 20k impressions per day, past week impressions started to change. And now I have 399 impressions per 24 hours :/

_alphageekon Show HN: Orch8 – Durable workflow engine in Rust, one binary, Postgres or SQLite

Amazing.Thank you

_alphageekon Show HN: Orch8 – Durable workflow engine in Rust, one binary, Postgres or SQLite

Thanks! Effect-ts is totally different tools though. It's an effect system (managing side effects, composability, error channels), while orch8 is about durable execution: your workflow survives crashes, deploys, and restarts without losing a single step. Currently LLM drives demand for the orchestration systems. As them requires loops under them to solve the real-world problems. The core principles under orch8 is to have llm heavy workflows, f.e. llm step is exists out of the box and it can modify execution at a runtime. Regarding which framework for non-tech users, in my humble opinion - non of the yet. Effect-ts requires steep learning, temporal requires understanding of determinism constraints. Even Inngest assumes you can write code. Declarative way of defining workflows, in my opinion is the way to og. As llm can generate declarations, workers, tests and run on their own. I shipped also cloud.orch8, one of the core ideas is the templates, where user can click through template - fill their variables and have their executions running with 0 code. but as ti is early development, I did not develop many templates yet - something I gonna focus next few months.In slightly far future I plan to ha templates marketplace, where people can earn money for designing solutions for other people/companies.

_alphageekon Show HN: Orch8 – Durable workflow engine in Rust, one binary, Postgres or SQLite

Hi HN,

TBH, it is my first post here and I am nervous a lot, as I put a lot of effort into this product. I have been building http://orch8.io for past 2 months. The story started when I used Temporal for long-running workflows, month after month/many months/years, and ended up in a situation where I needed to write lots of fixes around Temporal. Temporal is a great tool, covering most of the use cases, but for long-running execution, solution around it is starting to be very painful and too complex. Especially if the workflow can be updated 2-3 times during execution.

So I decided to go with Rust, as it is fast, small, and can have only 1 binary. Atm it supports only two dbs. SQLite for local development and PostgreSQL for scaling.

Workflows are defined as JSON blocks(sequences), and the engine persists state so work can resume after crashes or restarts.

Orch8 includes many workflow/application semantics out of the box. Instead of modeling everything as code-level activities, sequences use higher-level blocks directly:

- Semantics: Parallel, Race, TryCatch, Loop, ForEach, Router, SubSequence, and CancellationScope blocks

- A/B testing blocks for deterministic variant selection

- Built-in LLM calls across major providers(Also, LLM can inject additional steps into the current execution automatically, if you design the sequence that way)

- Human review(when smn need to approve before the decision. It is very helpful for early sequence development, where there might be bugs and you need to verify that you are not doing something wrong on critical blocks. One use case, I have connected it to a Telegram bot with the Polymarket test. I am getting information, should we go or not go with bet on analysis done with LLM and opportunities found )

- I decided to integrate ActivePieces out of the box. It allows to have 200+ connectors to different tools immediately and takes the burden of diversity of integrations off my shoulders

- Any non-standard behavior is done via external workers in any language via REST polling. Also, I shipped 3 SDKs: Node/Python/Go

- JSON-defined workflows that can be generated, versioned, and shipped without compiling worker code

- Overall I wrote 2000+ tests 1300 unit, 700 integration tests, and increasing coverage every week

Other pieces included today:

- Durable execution with retries, snapshots, and dead-letter handling

- Scheduling with relative delays, business-day rules, timezones, send windows, and cron triggers. F.e. never send sales emails on weekends; Or use jitter window of 4 hours to send 1000 emails.

- Rate-limit pools with rotation, daily caps, warmup ramps, and defer-on-limit behavior

- Added Prometheus metrics, structured logs, and audit logs(I did not test it well in prod, yet, might be some glitches)

- I have created a polymarket worker for anyone who wants to integrate it. It covers all API and is located in org repo.

Will be honest:

- This is not battle-tested at Temporal scale

- No visual workflow builder yet; sequences are JSON for now. Plan in the nearest future. But it is nice-to-have, I do not see crucial value in it. Thought, I have a vision how to make it different and friendlier than usual(thanks for 6 years of d3 development in early career)

- Still early, so I would not pretend every production edge case has been found. But I really appreciate contribution or suggestions.

License is BUSL-1.1. You can use it in production unless you are offering Orch8 itself as a competing hosted/embedded service. Each release converts to Apache 2.0 after 4 years.

Links:

- GitHub: https://github.com/orch8-io/engine

- Docs: https://orch8.io/docs

- Quick start: `docker run -d -p 8080:8080 ghcr.io/orch8-io/engine:latest`

I would like to hear from people who have used Temporal, Airflow, Prefect, Dagster, or homegrown workflow systems. What are your pain points now?

Thank you, guys.

With respect, Oleksii.

_alphageekon Async Rust never left the MVP state

The duplicate-state collapse (hoisting the match out of the await branches like in his process_command example) is the single easiest pattern anyone can apply to existing async code today. No compiler work needed, just a refactor.

_alphageekon Ask HN: Who wants to be hired? (May 2026)

  Location: Rio de Janero, BR -3 GMT
  Remote: Yes
  Willing to relocate: No
  Technologies: Rust, Nodejs, Typescript, React, Postgres, optimizations and mission critical systems, systems toolkits, exchanges, security audits
  Résumé/CV: www.ovasylenko.com
  Email: hello@ovasylenko.com
  Others: Get things done attitude

_alphageekon Vercel’s pricing page

I go full course with supabase, they have compatible S3 storages. So auth, db, storage in same place and pretty easy to manage.

_alphageekon I Built a WebAssembly Runtime in 5 Days

I would treat bwrap processes as single-use across tenants, since host-side state (allocator, FDs, namespace bits) accumulates and you can not really prove it clean.

_alphageekon "Parse, don't validate" through the years with C++

The C++11 example is the weakest in the article by its own thesis. Public throwing constructor, no year check, no leap-year check, so Birthdate(0, 2, 30) constructs cleanly. The C++17/23 shape (private ctor + static factory) is the actual mechanical insight from King's essay. Make the constructor a function that can fail, so the type itself carries the proof.

_alphageekon DRAM Crunch: Lessons for System Design

The weak link in the argument is the "1-2 GB DRAM stays stable" claim. That tier is stable because demand sits elsewhere. If AI workloads actually migrate down-capacity at scale (which is what the piece advocates), pricing pressure follows them.

_alphageekon Rust Memory Management: Ownership vs. Reference Counting

I would also mention Rc/Arc is Cow<T> -it lets you borrow by default and only clone when you actually mutate. It sits in the middle of the spectrum andsaves a lot of allocations in code paths where most callers just read.

_alphageekon Box to save memory in Rust

If anyone's doing this kind of optimization, dhat-rs is worth a look, it shows you exactly which fields and call sites are eating memory, instead of just a total. Saves a lot of guessing about where to start.

_alphageekon Reverse-engineering a supply chain attack delivered via fake Web3 job interview

These attacks evolve each year. Initial ones were just obfuscated code inside some utilities jest/tailwind config, most sophisticated I have seen obfuscated code loaded on chain. So you can not find any trace in the packages, but when you start/install it loads transaction info - decode and boom - you are hacked. So the safest way not to run it or run in isolated docker environment.

_alphageekon AI fact-checker with guardrail classifier and MCP server

How it is different from perplexity prompt?

_alphageekon Show HN: Minimal visual chess openings explorer

Really nice.

If I were you I would also develop interactive opening tree as an embeddable widget for chess blogs, coaches, and content creators. Chess.com is static one.

Also if you can get api/history of user plays - you can help them with analyze to get better openers and become better players.

_alphageekon How to make a fast dynamic language interpreter

Good writeup. The Arguments arc (#7→#13) hits close — did basically the same dance for an async step evaluator in Rust a while back. Went all in on Cow<'_, Input> assuming borrow-in-the-common-case would earn its keep. Microbenches looked great. Real workload: the Cow discriminant plus lifetime gunk bled into every combinator past the first await, inlining fell off a cliff, the whole point of Cow evaporated. Ripped it out for NoInput / OneInput<T> / MultiInput(Vec<T>) at the evaluator boundary — same split as your ZeroArguments / OneArgument / TwoArguments, just arrived at the ugly way. One thing I keep wondering: have you stacked arity specialization with type specialization on the native path? Binary<add, int, int> style, drops the isInt probe altogether. Guessing the code size math didn't work out, or ICs are already soaking up whatever's hot on the object side so the native fast paths don't matter much. Which one?