Algostonk

Finally someone organized my music collection, which probably started when I got my first MP3 player in elementary school. about 1,140 audio files after cleanup. These files survived countless PCs, my PSP era, Windows Mobile 6.1/6.5 HTC phones (few knows what it is today), random external drives, and 20+ years of append-only management. It has to fix: 1. GBK/GB18030 Chinese ID3 tags being read as Latin-1 mojibake. The major headache for morden players. 2. CUE split. 3. misc small issues like duplicate tracks, missing artist/album metadata, split album artists, and cursed folder names. @ChatGPTapp Thank you.

@sama :popcorn

homeboy you're the one sellling public market investors on short-term space datacenters

@jakevin7 关键词太多，触发简中互联网应激了

@algoxstonk 他之前的工作很 solid 的，不过互联网舆论是很可怕的，😮‍💨

李博杰喷 deepseek 的帖子火了。 deepseek其实本就是重视工程能力的团队，考验coding其实很正常。 另外现在 AI 时代，像阿酥这种会吹的会面试的太多了。大家都很怕遇到'阿酥们'，其实是可以理解。 新时代如何去面试确实是难题。

Small scope note: blcli is GCP-first today. That is intentional — we wanted to open-source the stack we actually run and battle-tested in production, not a thin multi-cloud abstraction that were freshly vibe-coded. We are happy Google Cloud customers, and this stack encodes a lot of real GCP + GKE + Terraform + k8s production practice.

We are open-sourcing blcli: an Agentic Infra Stack, battle-tested at 30M+ user scale. It allows coding agents like Codex or Claude Code to help manage your whole cloud infrastructure through code, PRs, dry-runs, and deterministic apply workflows. A solid & serious infra that can support to millions of users. This is a collaboration across multiple teams, the same stack that powers @AlvaApp, @Galxe, @GravityChain, and @ReahPlatform. Check it out here: Docs: blbl.gitbook.io/blcli-docs/ blcli: github.com/ggsrc/blcli Production stack template: github.com/ggsrc/bl-templ… Personal account starter: github.com/ggsrc/bl-templ… A common take today is: AI agents are useful for toy apps and prototypes, but not for serious infrastructure. The conclusion is wrong, because the issue is not that agents cannot work on real systems. The issue is that real infrastructure requires a large amount of expert context to get it correct in the first place, and even more context to guide agents through the next 18 months of iteration. Production infrastructure is not just a few Terraform files or Kubernetes YAMLs. It includes: cloud projects IAM boundaries networking VPC / subnet / firewall design Terraform state and backend management Kubernetes clusters cluster add-ons secrets management Git-based deployment workflows observability and telemetry (logs, metrics, traces. All integrated together and ready for your Agents to debug live on your prod env) databases, often self-hosted for cost efficiency and control environment separation: stg / beta / prd operational runbooks rollback paths production failure patterns Most of this knowledge usually lives in senior engineers’ heads, internal docs, shell scripts, Slack threads, old runbooks, and lessons learned from real incidents. If an agent does not have that context, of course it will build toy infrastructure. So the real question is: How do we package production infrastructure expertise into a form that AI agents can read, reason about, modify, and operate safely? That is what blcli does. At its core, blcli is a CLI tool plus a whole package of best practices of Infrastructure as Code. The key design principle is simple: Agents are already very good at reading and modifying code. So we make infrastructure code-first. The generated repo is intentionally self-explanatory. An agent can open the repo and understand what happened, and what's next. Who blcli is for? We built blcli for two types of users. 1. Product teams that need to scale beyond prototypes The first group is teams building real products that need infrastructure capable of growing beyond the prototype stage. These teams want the speed of AI-assisted development, but they cannot afford toy infrastructure. 2. Frontier labs and agent teams building self-improving systems The second group is frontier labs, data companies, and agent teams that need infrastructure not just to run applications, but to train, evaluate, and improve agents. If you are building coding agents, infra agents, or long-horizon autonomous systems, blcli stack is a good agent harness/env. Authors: @SiriJhui @p0pUBhv35I8308 @alvinFu1 @ryan4yin @algoxstonk

Something I re-realized recently: Trading is not just about ideas, theses, data, or finding alpha (ofc @AlvaApp help you do these) But for some 'new' traders, the things outside the numbers matter just as much: emotions, psychology, and mindset. This week I took a break and watched a World Cup game in the Bay Area: Algeria vs Jordan. I didn’t know either team well, so to make the game a bit more fun, I placed a small bet (if won, ticket is free) on Polymarket. At the time, Algeria was priced around 64% to win, while Jordan was around 15%. On paper, Algeria looked like the rational side. They rank higher. But once the game started, Jordan played way better than I expected. After a few great counterattacks, they scored first and took the lead for the first half. At that moment, I had this strange feeling that reality was moving toward the low-probability outcome. And it reminded me a lot of trading (when I lose money). Football has a lot of structure behind it: squad quality, tactics, historical data, coaching, matchups. The gap between a stronger team and a weaker team is real. But football also has very sparse rewards, just like trading or many things on earth. 90 minutes is often not enough for the true difference between two teams to fully show up. That is why the better team does not always win. You can make a decision that is right from a probability perspective. Your data, odds, and thesis can all make sense. And you can still lose on that one outcome. The hard part is not finding a 64% opportunity. The hard part is staying clear-headed when that 64% starts moving toward the other 36%. In the long run, what decides whether a trader survives may not just be their information edge. It may be whether they can live with probability, volatility, and their own emotions.

@0x_Aks We should build a LAB short collection lol. alva.ai/u/snarketh/pla…

$1 mln of $LAB exposure can be hedged by buying $9k puts Strike - $1 Expiry - Aug 21 LAB is currently trading at a FDV of $18 Bn which is very high! There is uncertainty in the market about $LAB price come the D-day i.e speculated token unlock at 14th Aug So, I have created this LAB Unlock Put War room on @AlvaApp AI agent If enough interest is there then I'll launch an options market on LAB for 21st Aug expiry with $1 strike price put options available for buying So, if you are someone like @skylinee then you can hedge your LAB investment and put sellers can earn solid premiums on this as volatility is very high for this. Find the link in comment below 👇

@caelynxx7 that server saves us $5000 / month, so I think it's fair to feed it some more good RAMs.

Last year, we deployed a used $DELL server in a local data center in SF, with total cost of less than $4000, 256GB RAM, 96 Cores, and more than 20TB (RAID5) disk, half NVMe SSD, half cheap HDD. At the time, 8 sticks of 32GB DDR4 2666MHz for a total of $248 — about $31 per stick. Now, our team have been spinning up way too many VMs for their agents, so I had to add more memory. So I went to the same seller and he told me this: “To be honest, when I looked back at your original order, it almost brought tears to my eyes remembering how great memory prices were at that time.” The current price: 8 × 32GB DDR4 2666MHz — $1,520. That is more than 6x the price. I told him i'm crying right now, and yes, the price is OKAY. The price $MU is OKAY.

wow, very cool. Just tried to ask it to teach me about slay the spire 2 Ironclad, the UI/UX looks cool, although the course content is unfortunately a bit sloppy (model's fault?)

lmao, you must be super bullish to be allowed to quit.

It’s going down, I’m yelling timber. You better move, you better dance. See lab:native going down live together here alva.ai/u/snarketh/pla…

@artinmemes 谢谢老板推荐。算法和数据准确性持续优化中，有什么问题欢迎DM！

顺着刚才evergreen的介绍，给大家推荐一个AI站，可以看美股kol们的喊单回测 有胜率榜，有收益榜，有单个最大收益榜，虽然统计有待商榷，但是个发现宝藏观点和博主的地方： alva.ai/u/zet/playbook… 后面我也会在这个榜里挑一些宝藏的千粉博主给大家推荐

it's time to accelerate. btw subscribe to this playbook so you can get notifications! (ofc I did already

@Oldman__Crypto join us! alva.ai/u/snarketh/pla…

#Lab Over $30M unlocking every day. Market cap of $15 billion. This shitcoin ranks #12, surpassing other projects. This shitcoin will drop below $1. Just like #Beat — it was priced at $8–10 and has already fallen close to $1

Alva should support those data too, similar to what massive has supported. Let me know if your Alva agents doesn't do the work. I just ask mine, she says she will 1. query contracts by underlying, e.g. AAPL 2. pick the OCC ticker, e.g. O:AAPL260410C00200000 3. fetch option kline data for that exact contract

@algoxstonk @Alva_ai Oh yea so ended up using massive.io started option tier which has the real time option data. Supposedly, it should be just laggin 5-15min, i need to go back and check it.

I just deployed a production-ready Options #GammaSqueeze Tracker on @alva_ai @algoxstonk It is a non-linear risk engine designed for high-beta volatility ($MU , $TSLA , $SNDK ): 1/ Tracks Dealer Gamma Wall & Net GEX Sign-Flips 2/ Pre-calculates 1-3 Sigma Expected Move 3/ Triggers IV Crush alarms prior to major catalysts Architected with minimum usable surface area. I kept the quantitative engine read-only and left the ticker fully fluid. Click Remix, swap the ticker to your likes, and automate your portfolio defense in 3 seconds. Live workflow here: alva.ai/u/decenfund/pl… #alvaai #optiontrading #stock

Alva dev is moving smooth because we built a super agent-friendly dev env: Infra: Terraform + k8s, fully infra as code, Atlantis, ArgoCD, and full suite of otels. All read/write-able to agents. Codebase: a monorepo built to be "local-first". Anyone can run the whole Alva backend on a laptop, or some freshly minted VM. Agents: Codex/Claude can work inside a real environment: research → plan → TDD code → review → PR → human + AI review → release checks for DB db migrations, env vars, secrets, and infra diffs. Will share & open source more when I have time.