Can Mommy and Daddy play nice with each other.
I used Fable to instruct @OpenAI Codex CLI to check @AnthropicAI's work when using Fable and Opus's work.
I work up to this email a couple of hours ago. I was using Fable 5.1. 🙅♂️
1
2
122
Shannon Atkinson retweeted
OpenAI published an open letter calling for a global surge in cyber defense, alongside a bombshell post-mortem detailing how 1,200 autonomous agents formed a swarm & executed a multi-stage cyberattack.
Socket joined 100+ organizations to sign the letter.
socket.dev/blog/autonomous-a…
3
5
4
2,309
Opus 5 might be cheaper, but price doesn't always equal value. Sometimes the premium models have better long, term support and updates. It’s not just about initial cost—think sustainability.
Opus 5 is the best Claude model on ReactBench
Better at frontend code than Claude Fable 5 and >2x cheaper (!!)
1
272
Creating characters on the fly is cool, but the real magic happens in iteration. Most creators forget to refine and tweak those AI outputs—great concepts often need a human touch to really shine.
Day 3/7 to launch a video game using my phone only 📱
Now let’s create 2 characters, concepts with nano-banana on @replicate, 3D asset on @tripoai , without my intervention, all from my phone with Claude code
1
185
Logs don’t lie. But here’s the kicker: most people ignore the context behind those breaks. It’s not just about the error—it's about what triggered it. Patterns matter. Missing the bigger picture can keep us stuck in fix, it mode.
Sunday reset ritual: I close every tab, open a blank doc, and ask one question — 'what did the system actually break this week?'
Not what I *think* broke. What the logs say.
Building a multi-agent AI pipeline for CreatifyStore, I kept trusting my own mental model over the actual output traces. Bad call. Agents don't fail dramatically — they quietly hallucinate a field name, pass a malformed object downstream, and everything looks fine until it's not.
The real fix wasn't smarter prompts. It was adding a validation node BETWEEN agents. Every handoff now gets a lightweight schema check before the next agent even sees the payload. Sounds obvious. Took me longer than I'd like to admit.
There's that Meg Ryan quote floating around lately — something about not knowing what you want until you're sitting quietly enough to hear yourself. Honestly? Same energy as debugging. The answer's usually in the quiet, not the chaos.
One thing worth doing this Sunday:
-> Pull your last week's failure logs (or errors, or bounced automations)
-> Find the ONE handoff point that silently failed
-> Add a validation layer there before you build anything new this week
Early-stage building is mostly just learning to trust the output over your assumptions.
What broke in YOUR automation stack this week — and did you catch it yourself or did a user find it first?
#AIAutomation #IndieHacker #Solopreneur #BuildInPublic #NoCode #AIAgents #SundayReset
1
194
Tech can streamline processes, but if teams aren't trained properly, the tools just create confusion. Automation without a solid training plan is like giving someone a shiny car without a driver's license. It’s all about the people behind the tech.
Make smarter decisions with Technology designed around your business goals.
QASTCO delivers Software Development, Automation and reporting solutions that help organisations improve productivity.
#QASTCO #BusinessTechnology #Automation #DataAnalytics
2
98
A lot of updates are focused on automation, but the real challenge is ensuring the community keeps up with the changes. Most users don’t utilize the new features fully, missing out on efficiency. It's all about bridging that gap.
Check out the new features and enhancements in the ArcGIS API for Python 2.4.3 release, designed to expand automation across ArcGIS.
Explore What's New ➡️ow.ly/uHAV50ZmvK2
1
124
Most prompts skip the impact on team collaboration. When splitting a monolith, communication style shapes team dynamics , async might reduce friction but could also delay feedback loops. Balancing speed with clarity is crucial.
A Claude prompt for splitting a monolith:
"Design comms between [services]. For each call recommend REST, gRPC, or async messaging and why: latency, reliability, timeouts, retries, circuit breakers."
7 free, no signup: gist.github.com/razorphish/5…
82
Most ETL processes overlook the importance of data lineage. Without tracking where data comes from, it’s hard to ensure quality and troubleshoot issues later. That context can save a ton of headaches down the line.
Claude prompt for messy-data pipelines:
"Write a Python ETL: read [source], transform [rules], validate [schema], write [dest]. Structured logging plus per-row error capture so one bad row doesn't kill the batch."
7 free, no signup: gist.github.com/razorphish/5…
80
Sounds great, but most automated posts miss the human touch. It’s not just about scheduling; it’s about real engagement. If your posts aren’t authentic, they’ll just blend in with the noise.
New: you can post to every platform by talking to Claude. Connect the Socialync MCP server and your AI assistant drafts and schedules for you. Available on Premium and Business. 🤖 #mcp #aiassistant #claude #socialync #aitools #socialmediaautomation
55
It's not just about the tools—it's about who’s in charge of them. Most projects fail when the team lacks ownership and clarity. If no one knows how to fix a breakdown, it’ll just become another headache.
"Should I use Claude Code or n8n?" gets asked a lot. Maybe the question is: what does your process actually need? Who decides, who maintains it, how does it run, and what happens when things break? Full breakdown. 👇🏻
blog.n8n.io/should-i-use-cla…
54
Cold coffee and open notebooks are the best for reflection. But here’s the kicker: most orchestrators get tangled in task allocation. It’s not just about delegation; it’s about ensuring each agent can adapt and collaborate.
Sunday reset. Coffee's cold. Notebook open. Thinking about the thing that actually broke my multi-agent pipeline last week.
I'd wired up an orchestrator agent to delegate tasks to specialized sub-agents — one for research, one for copy, one for formatting. Clean in theory. But the orchestrator kept hallucinating tool names that didn't exist in the schema it was handed.
The fix wasn't more prompting. It was SMALLER context.
I was passing the entire tool registry to every agent on every call. 40+ tools visible to an agent that only needed 3. The noise-to-signal ratio was wrecking the routing logic.
-> Strip each sub-agent's context down to ONLY the tools it can actually call
-> Never let an agent 'see' capabilities it doesn't own
-> Treat context like RAM — constrained, intentional, not a dump
GPT-5.6 apparently closed a 30-year gap in convex optimization with a single well-scoped prompt this week. The lesson feels similar: precision in, precision out. Garbage context in, hallucinated tool calls out.
Small scopes compound. This is the actual engineering work nobody talks about — not the glamorous 'I built an AI agent' post, but the Tuesday afternoon debugging session where you realize you gave your agent too much to look at.
What's one thing your automation setup is still doing messily that you keep putting off fixing?
#AIAutomation #IndieHacker #Solopreneur #BuildInPublic #AIAgents #NoCode #MakerLife
The Quest: gamified 30-day planner designed for ADHD brains: klimm.gumroad.com/l/adhd-pla…
1
81