Elson

Here's a workflow I use constantly when building features I don't fully understand yet: Start with the dumbest possible version. Like, embarrassingly simple. If you're adding search, just filter an array with.includes(). Get that working in 10 minutes. Click it, see it do the thing. This is your safety net. Now ask Claude: "Here's my basic search. Users will type fast and it needs to handle 10k items without lag. What's the next incremental improvement?" Key word: incremental. Not "build me production search." Just one step better. Claude might say "debounce the input." You add that. Test it. It works. You understand it because you wrote the simple version first. Then go again: "Works great, but now I need fuzzy matching for typos." Each cycle you're learning the actual problem space, not just copying solutions. And you always have a working version to fall back to. I've used this for payment flows, real-time features, data syncing, all of it. The velocity comes from never being stuck on a broken thing you don't understand. Simple first. Then iterate with AI. That's the whole workflow.

Workflow I use when prototyping unfamiliar APIs: 1. Ask Claude for the smallest working example 2. Run it 3. Paste back the first error with the code 4. Let it fix auth headers, missing params, or outdated endpoints 5. Add features one at a time The trick is starting with something that runs, even if it does almost nothing. I keep a scratch file called playground.*py for this. Once the prototype works, I clean it up and move it into the real project. Works well for payment APIs, email services, and SDKs where the docs either skip steps or assume you already know the ecosystem.

Source: OpenAI openai.com/index/introduc…

OpenAI just launched ChatGPT Images 2.0. The upgrade brings better text rendering (finally), multilingual support, and improved visual reasoning. Text rendering has been a weak point in most image models. Prompts like "put this exact phrase on a sign" usually fail or look garbled. If this actually works reliably, that's useful for mockups, prototypes, and UI sketches without needing a separate design tool. Multilingual support means prompts in other languages should generate correctly labeled images. Helpful if you're building localized content workflows. Visual reasoning is the more interesting part. If the model can interpret spatial relationships, object placement, or compositional logic better than before, it becomes more than a pretty picture generator. It starts handling design intent. Worth testing if you're automating design flows or prototyping UI. I'll run it through some real scenarios and see if it holds up.

@Polymarket I believe it.

JUST IN: Nike to reportedly cut 1,400 jobs, mostly from technology department.

Source: openai.com/academy/worksp…

OpenAI just published a guide on building workspace agents in ChatGPT. If you're automating workflows for your team, this walks through setup, tool connections, and how to scale agents across operations. Worth reading if you've been trying to figure out where ChatGPT agents fit in your stack vs. Building your own. The guide covers practical setup, not just concepts. Might save you a few hours of trial and error.

@nblxmacz @SadAlbert10 I just dragged the UI for my python script to the left

@Elsontec @SadAlbert10 Do you know how I can increase the context window?

i still dont see 5.5 in my codex

Source: openai.com/index/introduc…

OpenAI just announced GPT-5.5. They're calling it their smartest model yet, faster, built for complex tasks like coding, research, and data analysis across tools. If you're building on OpenAI, worth testing against your current prompt stack. "Smartest" is marketing, but if it actually handles multi-step reasoning better or doesn't hallucinate as much on code, that's real. I'd benchmark it on your hardest cases before assuming the upgrade matters. No pricing details yet, so watch for that before you rewrite everything.

Source: openai.com/index/introduc…

OpenAI just released Privacy Filter, an open-weight model for detecting and redacting PII in text. If you're building anything that processes user input. Support tools, chatbots, document pipelines. This is worth looking at. PII leakage is one of those quiet risks that only shows up when it's too late. They're claiming state-of-the-art accuracy, which matters because most redaction tools either miss edge cases or flag too much. The open-weight release means you can run it locally, which is the right call for sensitive workflows. Concrete use case: if you're logging LLM conversations for debugging, you probably want something like this in the pipeline before anything hits your database. Same if you're building agents that touch customer data. I haven't tested it yet, but the fact that it's open-weight and purpose-built for PII is a good signal. Most devs either roll their own regex (bad) or skip redaction entirely (worse). This gives you a real option.

Source: openai.com/index/gpt-5-5-…

OpenAI is running a bug bounty for bio-specific jailbreaks on GPT-5.5. They're paying up to $25k for universal prompts that bypass bio safety guardrails. This is red-teaming as a service, outsourced to anyone who wants to poke at it. If you've been curious about where model safety actually breaks, here's a way to test it with permission. The focus is narrow: bio risks, not general jailbreaks. They want prompts that work reliably, not one-off edge cases. Worth noting: GPT-5.5 isn't public yet, so this doubles as early access for researchers. If you're into adversarial testing or safety work, this is a cleaner path than guessing in prod.

Source: openai.com/index/introduc…

OpenAI just shipped workspace agents in ChatGPT. They're Codex-powered, run in the cloud, and automate workflows across tools. The interesting part: they're designed for team use with proper security controls. If you've been stitching together Zapier chains or custom scripts to connect your stack, this might actually consolidate some of that. Worth checking the docs to see what integrations they support and how the auth model works. I'm curious how they handle state and error recovery when a workflow spans multiple services. That's usually where these things fall apart.

Source: openai.com/index/speeding…

OpenAI just shared a real workflow case study on WebSockets in the Responses API. The Codex agent loop was hitting latency issues from repeated API calls. By switching to WebSockets + connection-scoped caching, they cut overhead and improved model response times. If you're running multi-turn agents, this pattern is worth studying. The overhead from opening new connections every turn adds up fast. WebSockets keep the connection alive, and the cache means repeated context doesn't re-bill or re-process. The post walks through the actual agent loop structure, which calls matter, and where the savings show up. Not theoretical. This is how they're running Codex internally now. Helpful if you're building anything that loops with an LLM more than once per user action.