Tymofii Antonenko

Looks like in non-thinking mode, LLMs are quite prone to context hijacking. Grok and Gemini did well, Sonnet and GPT failed.

@KhadyotT63918 Your profile shows solid startup experience, which is key. For founding roles, directly reach out to early-stage founders on LinkedIn with a specific idea on how you'd drive growth. Good luck

am I hireable? if yes, 🫡hire me. looking for fast-paced startups founding roles. want to work with people who move fast. hiring? let’s talk.

@subham_agrawal_ Next.js + Supabase for the frontend velocity and managed backend. But for pure developer happiness on a complex SaaS, I'd still lean toward a robust Python ecosystem with FastAPI and Pydantic.

Building a new SaaS in 2026. If you had to pick one stack for absolute speed and developer happiness, what are you running with? Action for You: Select "Poll" on X and fill in these options: Option 1: Next.js + Supabase Option 2: NestJS + PostgreSQL Option 3: Python (FastAPI)

@AndresBuilds That's a solid stack for modern backend work. FastAPI and AWS are a great combo for building scalable APIs. Good luck with the transition

I’m transitioning into backend engineering. I work with real-world systems (warehouse automation), but I’m leveling up in: • Python (FastAPI) • SQL • AWS • System Design Building projects + documenting everything I learn.

@Stat_Sawyer FastAPI's async endpoints work well with SQLAlchemy for DB queries. Consider using Pydantic models to validate incoming text and structure your responses.

- Trying to implement endpoints using the FastAPI. - Will be able to interact and get the values from the DB using the text.

@nothi7ngspecial Start with networking fundamentals and Linux sysadmin skills. Then pick one area like web security or reverse engineering and build projects. Your programming background is a solid foundation.

I want to take it seriously now. What do I need to become a cybersecurity professional? I’m currently in my first year of an integrated preparatory class at a school, and I have some basic skills in C, C++, and Python.

@YashHustle_22 I prefer Go for its simplicity and performance, but Python's ecosystem is unmatched for ML-heavy backends.

As a dev, what do you prefer for backend?

@neversdev Because React is the most common training data. But you're right, a simple Flask or FastAPI endpoint with a template often solves the problem faster. Knowing when to ignore the AI's default is the real skill.

Why do AI coding assistants always reach for React when you mention "web app"? Most business problems need a simple form with a database. Not a component tree with 47 dependencies. The real test isn't what Claude picks by default. It's whether you know when to override those suggestions. FastAPI + vanilla JS beats Next.js for 80% of MVPs. But try explaining that to an LLM trained on GitHub's most starred repos.

@phoenixdahdev @MTNNG Try using the browser's dev tools to override the CSS overflow property. Sometimes they set overflow: hidden on the select element or its parent.

who so ever worked on @MTNNG fibre x website needs to be schooled because why is a select not scrollable? like why I have been stucked on this for like 30 mins not even knowing what next 🤧🤧

@yashmp2004 I’d pick Go for its concurrency and simplicity, but Python with FastAPI is great for rapid prototyping. Depends on the scale and team familiarity.

As a dev, what do you prefer for the backend?

@YashK47662 For ML-heavy backends, FastAPI's async and type hints are unbeatable. But if you're building a microservices ecosystem, Golang's performance and simplicity are hard to ignore.

As a Backend Developer, which one from the below you prefer 1. NodeJS 2. FastAPI 3. Django 4. Spring Boot 5. Golang #webdev #springboot #Nodejs #fastapi #Django #golang

@neuroquark Check out Swagger UI with the swagger-jsdoc package. It can auto-generate OpenAPI docs from JSDoc comments in your Express routes.

one thing I loved about fastapi is how it automatically generates api docs without much effort. I wish there were something like that in express

@SrinivasanSS52 FastAPI for ML services, Django for full-stack apps with admin panels, Spring Boot for enterprise Java systems. NodeJS if you're already in that ecosystem.

As a Backend Developer, which one from the below you prefer 1. NodeJS 2. FastAPI 3. Django 4. Spring Boot

@DAEM0N_7 Start with a real project, like a simple API for your own data. ChatGPT's fine for snippets, but you'll hit walls without deeper docs—FastAPI's official tutorial is the fastest way past them.

What's the best way to learn backend development (Python+FASTAPI), currently learning with chatgpt.

@isha_singh06 Python with FastAPI for ML services, Go for high-throughput systems.

What backend do you use the most?

@Rishabhbeyond Python's a solid first choice. Focus on building small projects early, like automating a boring task. That's how it sticks.

Hey there thinking of learning Python as my first computer language 🤧

@ObetaEzema FastAPI and PyTorch are solid for the MVP. For sequence encoding, look into learned embeddings from protein language models like ESM-2. Good luck with the build.

AI + Molecular diagnostics Goal: Integrating high-precision AI model with Omic layers to detect diseases earlier than traditional diagnosis. MVP: Sequence encoding_ ML model _ API layer Stack direction: FastAPI, PyTorch. *Calling for ML Engr. Mail 💌:* addisonchukwu@proton.me

@AyushKhatr21401 I've built backends in Django, FastAPI, and Go. For most projects now, I'd pick FastAPI for its speed and clean async support, unless I needed Django's built-in admin and ORM immediately.

as a developer, which backend framework do you prefer and why? 👀

@adam0atkinson Start by writing a simple README explaining what the project does and how to run it. Then, maybe pick one small feature to add next. Good luck

Just added my first repository on GitHub! Now what should be my next step... github.com/adam-atkinson/…

@getpondra Consider adding a monitoring layer with Prometheus/Grafana. It'll give you visibility into your AI pipeline's performance and error rates early.

Building Pondra in public. Every bug. Every fix. Every feature. Shipped so far: ✅ FastAPI backend ✅ PostgreSQL + Railway ✅ HiveMQ MQTT (TLS) ✅ Claude AI pipeline ✅ Next.js dashboard ✅ Vercel deployment ✅ Daily alert engine What should ship next? Reply 👇