Automate Playwright Steps with BlinqIO AI Chat

AI has made one thing clear: answers alone don’t mean much anymore. When students can generate polished responses instantly, what matters is whether they truly understand the material and can explain their thinking. That’s why Khan Academy’s next step matters. By combining learning and assessment into a reflective process with Explain Your Thinking built in, Khan is shifting education back toward real understanding — not just getting the right answer, but showing the reasoning behind it. Take 30 minutes and watch this. Educators should pay attention. https://lnkd.in/g4XZMhzW Khan Academy #AIinEducation #EdTech #FutureOfLearning #EducationInnovation #CriticalThinking #StudentSuccess #LearningDesign #Assessment #KhanAcademy #DigitalLearning #TeachingAndLearning #EducationMatters #AI #HigherEducation #EdLeaders

More AI thoughts from me! Gasp! I did this long debugging session with a coworker just now along with a thirdparty vendor on Slack and Claude CLI up on my screenshare. Just saying. AI doesn't have to solve your problems, but it's awesome to have a sidekick along for the ride to provide commentary and do rubber-duck debugging sessions with. AI doesn't have to solve your problem, it can be just part of the solution and that's still a win. I can focus my attention on the other humans in the loop and delegate a bunch of stuff on the fly as we go. Good times!

Debugging: The art of fixing what you didn't know was broken, only to break what you thought was working. ⏱️⏳ Non-devs see a small feature update. Developers see the reality: 🔹 Time: Debugging almost always takes longer than the actual coding. 🔹 Chaos: A tiny tweak can ripple through the entire system. 🔹 Mystery: Error messages rarely hand you the answer on a silver platter. 🔹 Validation: Testing, breaking, and testing again just to be 100% sure. Coding is the visible part of the job, but debugging is where the real engineering happens. Respect the process! 💻 Tired of debugging blind? Stop guessing and start building with total visibility. Grab your spot for the Reliable AI agent creation with observability live session: https://lnkd.in/gWXEq796

A quick follow-up on Burla, because the interesting part is not just that I released a library. It is why the API ended up like this. Toy examples flatter mocking libraries. Migration is where they get judged properly. That is where the pain lives: - async-heavy tests - ref / out edge cases - verification styles nobody wants to review - loose defaults hiding mistakes - inconsistent APIs that trip both humans and AI tools The design bets in Burla are intentionally opinionated. If you want to evaluate it properly, do not start with the easiest test in your suite. Start with one awkward one. Find the link to the blog post in the comments 👇

I’m impressed by this character consistency workflow that uses a single GPT Image 2.0 character sheet and turns it into a multi-shot cinematic sequence in Seedance 2.0. We tried it ourselves on our own character and cinematic sequence, and the results looked solid. The consistency is not 100% as the Seedance model does hallucinate a little bit but it’s pretty darn close. Prompt structures in comments. Super cool workflow Nexora (@frametheory058 on X), thanks for sharing!

99 bugs in the code, take one down, patch it around… 127 bugs in the code. The code was working perfectly until I understood how it worked.

All those hours with breakpoints and console.logs weren't wasted. They built something most developers don't notice until they start debugging with AI: the ability to find the right evidence, in the right layer, at the right moment. That skill is what makes an agent useful when something breaks. 1. Knowing which layer to interrogate. You decide where to look. The agent reasons from what you bring. 2. Reading the gap between code and runtime. Describing that precisely is what gives the agent ground truth instead of a symptom to guess from. 3. Forming a hypothesis before asking. The engineers who get the right answer fastest narrow the space before they ask. Same muscle, different tool. You spent years learning to see what was actually happening inside a running system. That's exactly what an agent needs from you.

Power tools don’t cut straight without a jig—Claude Code is no different. If your first session felt meh, you probably lacked session design. Here’s a playbook that works: - Switch the mental model: Claude Code is a fast junior engineer. You set decision rights; it executes within guardrails. - Write a one‑page Mission Brief: user, outcome, non‑goals, constraints, risks, exit criteria. No code until this exists. - Add a Context Contract to your repo: stack, naming, interfaces, error shapes, security rules. Pin it to every prompt. - Lead with verification: tiny smoke tests, lints, and a staging deploy before any feature. - Run 10‑minute loops: plan → test → change → critique. By loop three, if diffs aren’t mergeable, reset the brief. - Demand rationale, not just code: every PR should include “why,” tradeoffs, and known gaps. - Measure what matters: mergeable PRs per hour—not tokens or LOC. Do this and the model compounds your effort instead of spinning cycles. #AIEngineering #ClaudeCode #DeveloperExperience Which single guardrail would most upgrade your next AI coding hour—clearer brief, earlier tests, or rationale‑first reviews?

I just read a paper that reframes how we should build agent skills. Current approach: hand-write a prompt, generate it once, or let the agent loosely revise itself. None of those reliably improve. Most degrade. SkillOpt treats the skill document as external state to be trained. Not written. A separate optimizer model proposes bounded add, delete, and replace edits. A validation gate only accepts changes that improve a held-out score. Results on GPT-5.5: +23.5 points over no-skill baseline. +24.8 inside Codex. +19.1 inside Claude Code. It beat human-written skills, one-shot LLM skills, and five competing methods across all 52 evaluated cells. The optimized artifact is a compact markdown file. It transfers across model scales and execution harnesses. Zero inference-time cost. The shift feels familiar. We moved from hand-coding features to gradient descent. Now we are moving from prompt engineering to text-space optimization. How long until your team's agent skills are obsoleted by trained ones?

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