You hit the nail on the head. The issue is that most memory systems treat all data points with equal 'weight.' A professional builder needs to implement a Time-Decay Function in their RAG (Retrieval-Augmented Generation) stack. If a topic hasn't been mentioned in 30 days, its 'relevance score' should drop. Memory should be a sliding window, not a permanent filing cabinet. Without a 'forgetting' mechanism, the AI's 'vibe' just becomes cluttered
Query re-writing is now live on Voiceflow's KB.
Before searching, it rewrites the user's question into something cleaner so the agent actually pulls the right answer.
Small update. Real difference in response quality.
Users never search the way your docs are written.
Voiceflow just shipped query re-writing it rewrites the user's message into a clean search query before it hits your KB.
Fewer bad answers. No extra setup.
You're scared to push updates to your live AI agent.
Voiceflow just fixed that.
Environments lets you clone your agent, split live traffic, test, and merge when the data confirms it's better.
A cron job runs the same command every night. A Claude Code Routine reads your issues, understands what changed, decides what matters, and posts a summary to Slack
Claude Code just connected to NotebookLM and turned a research topic into a podcast. No copy-paste, no tab switching. You type the topic, it handles the rest.
Liam Ottley just emailed me a 3-step formula to land your first AI client.
Capabilities β Contact β Convert.
Most people skip straight to Convert. That's why nothing works.
https://t.co/Y9qXdYtBqr just dropped a free course on how transformers actually work hallucinations, KV caching, inference speed. I've been waiting for something like this.
You can now build a voice agent that translates 70 languages live mid-conversation.
OpenAI's new Realtime API handles it as speech happens not after the sentence ends.
The doomsday posts about AI aren't really about AI. They're grief.
People built their businesses around access to talent, capital, and technical infrastructure. AI just made all of that available to anyone with a laptop. So the people who had those advantages are watching them π«₯
Andrew Ng just said AI won't kill jobs. He might be right and the reason is https://t.co/5kAxbQ2qVH AI labs hype job replacement to justify $10k/year pricing. Real data? US unemployment sits at 4.3%. Software engineer hiring is still strong.
The jobpocalypse was never about work
I was building client content workflows. Then HeyGen dropped Avatar V and I had to rethink everything.
15 features in April alone. Digital Twin is the one nobody's talking about yet.
Got an email this morning that made me stop scrolling.
Airtable just dropped a Claude connector. You think in Claude, you execute in Airtable. That's the whole pitch and it actually makes sense for client work.
Someone in my dev community asked what VS Code extensions we actually use.
My answer: Codeium for autocomplete, GitLens for blame/history, REST Client to test webhooks without leaving the editor.
What's in your setup?
Most AI automations fail not because the prompt is bad. It's because everything's jammed into one.
Split it:
β Normalize input
β LLM processes
β Format output
3 steps beats 1 "smart" prompt. Every time.
A founder described his AWS stack to me.
$2,800/month. "Not sure where it's going."
I ran it through a tool I built yesterday.
Found $800/month in waste in about 8 seconds.
The mistakes weren't complicated.
RDS Multi-AZ on a staging environment β $140/month for redundancy nobody needs.
NAT Gateway routing all S3 traffic β $0.045/GB when VPC endpoints are literally free.
12 Lambda functions with provisioned concurrency. Most got under 100 requests a day.
Nobody had looked at the bill in 6 months.
I'd been messing around with the Claude API the night before.
Wanted to see if I could call it directly from a plain HTML file no backend, no framework, no deployment, nothing.
Turns out you can.
So I kept going.
The idea was simple:
Describe your stack in plain text. Claude scans it, returns structured JSON. The UI parses it into a full audit report.
Every issue ranked by severity. Dollar estimates per problem. Actual fix commands not "consider optimizing." Real AWS CLI.
A prioritized action plan sorted by ROI.
One bug that got me:
Claude was returning 2,800+ characters of JSON and I had max_tokens set to 1,000.
Response was getting cut mid-string. JSON parser was dying. Took me longer than I'd like to admit to figure out what was happening.
One line fix.
max_tokens: 4000
The part that stuck with me:
The expensive mistakes are almost never technical.
It's always the boring stuff. Staging databases with production-level redundancy. Logging everything at 30-day retention by default. Services nobody turned off.
The bill creeps up $50 at a time until it's $800/month in waste and nobody knows why.
Built it in one afternoon. It's a single HTML file.
I called it CostRadar.
Demo video below. If you want the file, reply or DM happy to share it.
And if you paste your stack in the replies, I'll run it through.
Curious what it finds.
"Hey [Name], looks like we missed each other! Assuming something came up β want to reschedule for [Time A] or [Time B]?"
that's it
no guilt tripping. no "just checking in" energy. give them a graceful out and they almost always rebook
no-shows used to kill my momentum
i'd get annoyed, send a passive follow-up, and lose the client entirely
now i have a rule: the 10-minute ghost protocol
if they're not there after 10 mins, i send this: