Joshscript🧧 @joshscripts

Agent loop termination is the most under-engineered part of most agent systems. When should the agent stop? 👉Wrong answers: when the model says it's done. After a fixed number of steps. 👉Correct answers: When a verifiable success condition is met — tests pass, output validates, user confirms. When a maximum step budget is reached AND a graceful partial result is returned. When a dead-end is detected — the same tool called with the same input twice. When confidence drops below a threshold — return what you have with uncertainty flagged. An agent without clear termination conditions runs indefinitely, burns tokens, and eventually returns garbage confidently. Define success criteria before you build the agent loop. The loop runs until success OR exhaustion. Never indefinitely.

The sky is the limit of those who show up daily. Gm builders 🌄 What are you working on today?

@Gavel_on_X 😪

Most people build LLM applications by writing prompts manually. You tweak the wording. Test it. Tweak again. Test again. Then the model updates and everything breaks. Back to square one. There is a better way. It is called DSPy - Stanford's open source framework for programming LLMs with code instead of prompts. Instead of writing "You are a helpful assistant that..." you write Python code that defines what you want the model to do. DSPy then automatically optimizes the prompts for you. It runs experiments. Measures what works. Finds the best prompt automatically. You stop guessing. The framework figures it out. And because the logic lives in code not in fragile hand written prompts your pipeline does not break every time a model updates. It is reproducible. Testable. Scalable. The difference between DSPy and manual prompting is the same difference between writing software and writing sticky notes. Both work. Only one scales. Free. Open source. Built by Stanford. If you are building serious LLM applications in 2026 and you are still manually tweaking prompts look into DSPy. `pip install dspy`

@tz_daniels Absolutely

GM techies 🧑‍💻 Another day to ship something that moves the needle. Or fix what you broke yesterday. Let's make it count 🚀

System prompt best practices that actually make a difference in production: 1/ Be specific about format. Return a JSON object with keys: root_cause, fix, confidence" beats "return structured data. 2/ Define the failure mode. Tell the model what to do when it's uncertain. If you don't, it guesses confidently. 3/ Give examples for edge cases. One concrete example is worth five lines of description. 4/ State constraints explicitly. Only reference code that appears in the provided context. Do not invent function names. 5/ Version your system prompt. Store it in version control. Log which version was used for each request. You can't debug prompt regressions without version history. Most system prompts are written once and never revisited. Your system prompt is software. Maintain it like software. #ai #aiEngineer

@OhazBuilds True

@nuel_flow Gmgm Have a productive day bro

@tz_daniels Feel you boss 🙌

@skill_of_life True

CSS will-change tells the browser that an element will animate so it can prepare. The browser promotes the element to its own GPU layer before the animation starts. No jank on the first frame. The common misuse: putting will-change on everything. Each promoted layer consumes memory. Use it only on elements you know will animate. Remove it after the animation completes for elements that animate conditionally.

The current AI workflow has a precision tax. Every new chat starts slightly stupid until you rebuild the context. KBrain is my attempt to make that context portable: your sources, your permissions, your project state, available inside the AI tool you already use. Still early.

@Gabriel94989231 Gmgm

@MS_On_This @frontend__fairy Gm bro

one piece of advice you’d give to someone who is actively looking for a job right now?

@rivestack @Kyriakos_Pelek 👍

The vector database landscape in 2026 — No hype, just what to use and when: 🟤 pgvector — Postgres extension. Already on Postgres? Start here. Zero new infrastructure. Handles most RAG use cases at early scale. 🟤 Pinecone — Fully managed, serverless. Fast setup, auto-scaling, no ops. Best when you want production vector search without managing infrastructure. 🟤 Qdrant — Open source, self-hostable, strong hybrid search (vector + keyword + filters). Best when query flexibility matters more than managed simplicity. 🟤 Weaviate — Open source + managed cloud. GraphQL API, multi-modal support. Best for complex data types beyond pure text. The decision tree is simple: Building your first RAG system? → pgvector. Hit pgvector's limits? → Pick a dedicated DB based on your ops and query needs. Don't over-engineer the data layer before you've validated the product.

@DanielSmidstrup Crazy my pc still runs on 16gb ram. 🤦‍♂️ Looking forward to upgrade tho

@XFreeze Seeing a rocket this massive fly itself is already a miracle. ​But catching it mid-air with giant mechanical chopsticks? Three times now? ​SpaceX is making sci-fi look like standard engineering

@WatcherGuru Someone pls check on $BTC 🥲