Breaking Into Robotics? Master these 5 STEPS. STEP 01: Build Production-Grade Robots Learn how to Build Production-Grade robots. Robots that are reliable, scalable, efficient, designed around real use-cases, and ready for edge-cases in the field. Because in robotics, “it went down the staircase" or " was blinded by sunlight" can cost you a deal. ________ STEP 02: Master Robot-To-Revenue (GTM) Framework Learn how to launch, sell, deploy, and scale production-grade robot fleets. This is the part most students never learn. A) How do you turn a demo into a deal? B) How do you manage sensor blind spots, staircases, tunnels, WiFi outages, power outages, and complex customer sites? C) How do you integrate robots with cameras, doors, elevators, badge scanners, APIs, and operations workflows? This is where most robotics engineers struggle. They master ROS, but not GTM. They don't master customers. They can't convert demos into deals. So, master GTM frameworks. ________ STEP 03: Position Yourself as a Problem Solver Get good at demonstrating your value. You need to come across as someone who can help build, launch, and scale production-grade robots. Someone who is not just a job-seeker. Someone who can solve real-world problems in the field for leading robotics startups and convert demos to deals. That difference matters. A lot. ________ STEP 04: Lead with Value, Not Volume Value > Volume. Do not just apply to 100 jobs. Instead, identify a few startups. Ones that are aligned with your skills. Study their bottlenecks. Define your approach. Reach out to founders 1:1. Present your solution. Craft & convert your interviews. Solve A problems once you are in. ________ STEP 05: Address Your Bottleneck Ask yourself: What is your A bottleneck? For some, it's STEP 01. For others, it's STEP 02-04. ✅ For students who need help with STEP 01, our Applied Robotics Certification Program is designed to address that exact pain point. ✅ For students who need help with STEPS 02-04, our book, Decoding Robots offers the exact frameworks and templates you need to surgically position your profile & land high-growth opportunities in robotics. ________ ✅ SUMMER SCHOLARSHIP We understand that many students are serious about landing a job and building a successful career in robotics, but access to the right approach and field-tested frameworks has been a real bottleneck. For some, that constraint is financial. To address that barrier, we are launching a limited-time Summer 2026 Boring Sage Scholarship for our Applied Robotics Certification Program. Eligible students will receive a scholarship covering 90% of the program fee, bringing the final cost down to $19.95 per student. To apply, please complete the scholarship form below. 👉 Boring Sage Scholarship: Link in the comments. #Robot #Robotics

interesting shift, already mapping our APIs to MCP for agent access

React style immutable virtual three binding is inherently bad for memory as you can't optimize your memory layout at all. Also using static apis for dependency injection(React.use functions) makes multi threading extremely hard.

TO BECOME AN AI ENGINEER YOU MUST HAVE A CLEAR ROADMAP OF NEXT 3-6 MONTHS I just made one for you. There are 4 foundations the roadmap says you actually need. Clean Python. API literacy (reading docs, handling responses, debugging). LLM frameworks like LangChain or LlamaIndex. And system design intuition, meaning you know how to connect models, databases, APIs, and UI into something that works. That's the whole base. Everything else is just phases. Phase 1 is Python foundation. 4 to 6 weeks. Syntax, OOP, file handling, async basics. Phase 2 is your first API calls. 1 to 2 weeks. HTTP methods, headers, auth, JSON in and out. Phase 3 is core concepts. 3 to 4 weeks. How LLMs actually work, prompting techniques, RAG, vectors, embeddings. Phase 4 is portfolio building. 6 to 10 weeks. Pick 5 project ideas, build them end to end, add memory and agents, push everything to GitHub. Phase 5 is deployment. 2 to 3 weeks. Streamlit, FastAPI, Railway, Render, databases, monitoring. Phase 6 is job search. 4 to 8 weeks. Resume, GitHub polish, networking, interviews, applying with focus. Here is my article that covers it.

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A 22-YEAR-OLD CHINESE DEVELOPER LINKED 8 GMKTEC EVO-X2 MINI PCS INTO A 1TB AI FARM AND STARTED SELLING PRIVATE AI ACCESS FOR $6,000/MO each evo-x2 has 128gb of unified memory. eight of them on one table gives him 1tb of memory, no server rack, no rented cloud gpus, no $20k workstation sitting in a data center. the chip inside is amd’s ryzen ai max 395. one box can run qwen3 235b locally, while a small cluster has enough memory for deepseek-class workloads most consumer pcs cannot even load. the business is not the hardware. he sells private local inference to small teams that don’t want their code, client files or internal docs passing through public ai APIs. at $150–300 per seat, 24 users gets him close to $6,000/month. the full stack costs roughly $14k–16k, so the whole table can pay itself back in 3–4 months if it stays busy. for heavy ai users, the math is brutal. $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini turns into $5,280 a year before you even ship anything. install ollama, connect open webui, point the workflow at localhost. same chat interface, same coding flow, except the data stays in the room and every prompt stops becoming a small invoice. this is where local ai gets weird. a random developer with 8 mini pcs can now build something that looks less like a desk setup and more like a private cloud.

Zyloo enables developers to connect with models from multiple providers, simplify integrations, optimize costs, and scale applications faster without managing separate APIs and billing systems. zyloo.io/

I already have a list of APIs and need to familiarize myself with the SDK functions. I am doing a lot of work in C and C# (C Sharp) and need to create the APIs before I even get started building the VAST architecture and footprint.

One Skill Every AI Engineer Should Learn! Connecting LLMs to Live APIs. Here's a practical tutorial: amanxai.com/2026/04/12/conne…

GitHub's top 3 all-time repos: @freeCodeCamp (437.9k), public-apis (411.9k), free-programming-books (384k). Combined: 1.23M stars — more than @reactjs Vue Next TypeScript combined. None of them ship code you'd ever import. The star button isn't a quality metric. It's a save-for-later with a public counter — and the leaderboard finally made that impossible to deny. #OpenSource #WebDev

ByteDance's new open-source AI controls your entire computer. 100% local. Most desktop agents today rely on cloud APIs. Your screen, clicks, and files all leave the machine. ByteDance just open-sourced TARS, a multimodal agent stack that runs fully on your computer. It hit 36k stars on GitHub. The system uses a vision-language model to see pixels directly. It then drives your mouse and keyboard through plain English instructions. No screen reader. No accessibility hooks. Just raw visual grounding across any app on Windows, MacOS, or a browser. Screenshots, recognition, and execution all stay local. Two pieces ship together: 1. CLI plus Web UI frontend 2. Native desktop GUI operator The kernel is built on MCP, plugging into real-world tools without custom glue. Hybrid browser control switches between visual targeting and DOM parsing mid-task. One prompt can book flights, edit spreadsheets, or scrape dashboards offline.

APIS MAUUU

Natürlich sind das auch Bienen, ebenso im Grunde die Hummeln, aber wenn Imker von Bienen reden, meinen sie halt nur ihre Zuchtformen der Honigbiene Apis mellifera. Deren künstliche Überpopulation die Wildbienen bedroht.

Went to the first @openbuilderslx event last week. Always cool to be in a room full of people talking about things they built and shipped. Signed up to present at the next one in July. Going to show off some awesome products and APIs we’re building at @frontal_dev .

Yes exactly, most devs doesn’t think about scalability while designing APIs and that’s where system breaks at 1 billion rows. Cursor based approach fits here

There is no inevitability in AI. We all have agency in what comes next: Path 1: closed-source APIs, concentration of power, and a future decided by a handful of people in Silicon Valley and DC Path 2: open-source AI, where everyone gets to participate, own, and build together, including orgs like the city of Rio. Pick your path anon!

Test your functions, webhooks, APIs, log viewer and integrations, right from your workspace command center !test it live and see results and have improvements come back almost instantly !!! Come have a tour !! This command center is not like anything you have seen!

Tip: LLMs are good for R&D if you learn to phrase things properly and specify what personality they should use (I use "terse personality" a lot) E.x.: Grok and ChatGPT love to give me full, one-shot services that do specific things, like set up a pocketbase database and 'deploy' it using systemd, instead of dockerizing every bloody instance :) I come up with things like this example all the time. I'm constantly trying to make software stacks slimmers, and LLMs can help that creative process along, thankfully, because they're trained on Stack Overflow, Medium, y-combinator, etc. and can help me find these once "niche" ideas. I'm building a cli that can spawn infinite pocketbase instances so I can build quickie APIs really fast for my clients w/o hardcoding everything or ralph-looping my way to a slop product and rewarding Scam Altman so he can steal Scarlett Johanson's voice...

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7. Most projects talk about adoption. Pokedev is building the tools that can enable it: • SDKs
• APIs
• NFT validation
• Dynamic metadata management
• Developer integrations A much stronger infrastructure play than a typical meme token.