Luis B. Mata

Who is the modern Maradona? Who survives the bracket? Who actually lifts the trophy? We asked the database. `EMBED()` inside `INSERT`. `COSINE_SIMILARITY` finds the legend's modern echo. Cypher walks the rivalry graph. 10,000 Monte Carlo tournaments. One SQL surface.

@xoaanya I build SynapCores CE unified data for agents! SynapCores.com

Hey founders!!! Looking to connect with people building in: 💻 SaaS ⚙️ Tech 🤖 Automation 🧠 AI tools 📦 Product Development 🌐 Web apps Drop what you're working on 👇🏼

Thank you, wow, to what we have become. But I agreed that there is so much automation going on that we now don't even know what is real and what's not. We should have a social media platform where all bots are banned! And only humans are allowed to interact with each other! Synapcores, where data meets intelligence!

reply if ur not a bot

@vivoplt Take a look at SynapCores CE AI database , Graph + Vector + LLM + MCP. 
One database. One query.

@thenowhereway Devansh, Build semantic and permanent memory for openClaw as well as AI native database

Twitter is cool. But it’s 100x better when your timeline is full with people who code and build things. I need to connect with more founders and tech people. If you’re into Tech, AI, Startups, Design, web dev, SaaS, or programming, say hi.

@StarMapEngine @alex_prompter That’s why agents need to have dynamic permanent memory. If not as you said everything breaks after few turns

@alex_prompter soul.md is useful, but the cursed part is keeping it alive after the first run otherwise it turns into a little shrine the agent politely reads before immediately forgetting what happened yesterday 😭

I just broke down the anatomy of the perfect SOUL. md file for AI agents. SOUL. md is the identity file every AI agent reads before it does anything else. Without it, your agent is just a raw LLM with no memory, no personality, and no boundaries. With it, your agent knows who it is, how to talk, what to refuse, and which tools to use. Here are the 9 sections that make a SOUL. md actually work: → Identity (who the agent IS, not what it does) → Values (decision-making when rules don't cover it) → Communication Style (tone, length, formality) → Expertise (specific tools and domains, not vague "knows things") → Boundaries (the immune system. Holds even under pressure) → Workflow (step-by-step process for every task) → Tool Usage (WHEN and HOW, not just which ones exist) → Memory Policy (what persists, what gets wiped) → Example Interactions (one good example beats 10 abstract rules) Most people write "Be helpful and professional." That describes nothing. Every AI already tries to do that. The agents that actually work have SOUL. md files with real opinions, specific limits, and concrete examples of what "good" looks like. A strong SOUL. md is 200-500 words. Shorter = sharper agent. Save this. You'll need it the moment you build your first agent.

TradeGraph: NBA trade counterfactuals on one SQL surface | SynapCores AIDB youtu.be/u4vKtYrsWv4?si… via @YouTube

@BTCqzy1 Wao! That is amazing, well done. That was one of the moats we are solving as well with openClaw, memory!. So we created permanent semantic memory and released it as open source. Congrats!

AI 编程最让人崩溃的瞬间：它刚帮你修好了 A Bug，转头就把你原本跑得好好的 B 功能改废了。 这种 修一崩三 的死循环，是因为目前的 AI 编程助手，本质上都只是「指哪打哪」的实习生。它们看不见代码之间的深层依赖： 函数调用链、跨文件关联、状态流转、业务逻辑隐含约束…… 分享一个我在用的AI 编程神器：GitNexus（git 3万🌟） 它通过在本地构建语义知识图谱，将零散的文件转化为互联的逻辑实体。通过 MCP，为 Cursor 和 Claude 等提供了一套原生的架构索引～ 硬核亮点： ✅探索速度提升 77%，无效调用减少 94%，Token 大幅降低 ✅自动生成全局代码关系网，AI 拥有上帝视角，再也不 改A坏B ✅100% 本地运行，零上传，隐私安全，支持 Win/Mac/Linux 这不只是一个工具，它是把 AI 编程从概率预测拉向逻辑推理的关键一步。 地址：github.com/abhigyanpatwar…

The reality is that OS will come pre-loaded with SLM and be agentic in nature (Microsoft Windows, Ubuntu, MacOS), so there would be no need to pay the Croq, OpenAI, etc. So, yes, the whole madness of building Data centers and power-hungry GPUs will eventually fade away! Has no one heard of distributed systems?

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

@DataChaz @VelobaseX @DataChaz Who ever came up with the post idea gets a 10! Congrats!

HUGE WIN FOR OPEN-SOURCE If you want to see what an 8-figure ARR stack looks like, @VelobaseX just made theirs public. You get usage-based billing, affiliates, even server-side attribution for Ads & 𝕏 👀 Monetization is the new moat. Fork and win.

@sama Until we don't shift the paradigm of static models, and switch to plasticity, the only thing we are going to have are very smart automation.

what problem do you most hope AI will solve in the future? maybe we can help!

@openclaw Install: openclaw plugins install clawhub:@synapcores/openclaw-memory Security audit: Pass. Source-available. Free. github.com/SynapCores/syn…

Shipped: a long-term memory plugin for @openclaw backed by SynapCores AIDB. Drop-in alternative to the LanceDB memory plugin — same auto-recall/auto-capture lifecycle, plus three things it can't do: → SQL-filtered semantic recall → graph-relation walks → AutoML relevance scoring One binary. Live on ClawHub now. @openclaw @OpenAI

Want to see the technology behind? Watch this and five other 60-second demos → synapcores.com/demos

Who is the modern Maradona? Who survives the bracket? Who actually lifts the trophy? We asked the database. `EMBED()` inside `INSERT`. `COSINE_SIMILARITY` finds the legend's modern echo. Cypher walks the rivalry graph. 10,000 Monte Carlo tournaments. One SQL surface.

Five more 60-second demos — phishing, fraud, LLM safety, pharma, finance → synapcores.com/demos

What if your database could rewind the LeBron-to-Lakers trade? SynapCores walks the 4-deep cascade and scores the 2019-20 Bucks at 61% in the alt-timeline. (Actual: 18%.) Graph + vectors + ridge regression. One SQL query. #NBA #LeBron

"Why Feature Stores Didn't Fix Training–Serving Skew" by Luis M #DEVCommunity dev.to/synapcores/why…

"Why Feature Stores Didn't Fix Training–Serving Skew" by Luis M #DEVCommunity dev.to/synapcores/why…