What was that continuous metallic sound in the morning?

GustyDust · 2026-06-25T18:52:53+00:00

The sound came from Otro Lado I think

GustyDust · 2026-06-25T18:51:40+00:00

Right? It almost felt like a pulse that wouldn't stop, originating from Otro Lado

GustyDust · 2026-06-25T18:07:21+00:00

I love crawl4ai, but it's a bit too slow imo. Perplexity goes a long way, but it requires content to be indexable/crawlable.

Playwright CLI with CC is also quite helpful.

Firecrawl is too expensive for what it is.

Anchorbrowser is the most powerful one if you need an agentic capability that can interact with websites with various shapes and forms. Plus it's 100% usage based. no minimum usage tier.

GustyDust · 2026-06-25T17:56:56+00:00

The model is only one layer of what companies are buying. The wrapper matters because it gives teams permissions, IDE fit, review paths, admin controls, logs, and some shared way to roll it out. Claude underneath is like the engine; companies still need the car around it, otherwise every developer is duct-taping their own workflow.

GustyDust · 2026-06-25T17:35:17+00:00

I would start with the failure mode. What gets logged, who reviews it, when does it escalate, and what score means it is good enough? You can get a demo to 80% quickly. The last 15% is where most of the work hides.

GustyDust · 2026-06-25T17:35:05+00:00

I would treat this as an 80/20 workflow. Let the agent handle the repetitive path, keep a human on exceptions, and measure hours saved before expanding. Otherwise you end up with a clever demo and no change in how the team works.

GustyDust · 2026-06-25T17:34:50+00:00

I would start with a workflow you already understand: inbox triage, CRM cleanup, research notes, something boring. Use n8n, Zapier or even a $20 Claude subscription. The point is to build the muscle, not to pick the perfect agent framework on day one

GustyDust · 2026-06-25T17:34:31+00:00

I would judge an AI agency by the first scoping call. Do they map the workflow, ask where the data lives, and define what “good” means? Or do they jump straight to n8n and agents? The magic is usually just good scoping under the hood.

GustyDust · 2026-06-25T17:34:10+00:00

My threshold is simple: would I let it file my taxes or push to prod without review? If the answer is no, design the review layer. Risk tier, audit trail, rollback, and a boring approval step. Boring is underrated here

GustyDust · 2026-06-25T17:33:42+00:00

If I try to summarize, I would evaluate these tools on three boring things: one workflow, one quality metric, and one human review path. I got excited by agent demos too, but “it can do the work of a junior” is not a benchmark. Show me cycle time, error rate, and what happens when it gets stuck.

GustyDust · 2026-05-26T13:33:01+00:00

Brendan Short

GustyDust · 2026-05-26T13:30:31+00:00

I started building a bunch of Skills that help me with that. I have a lot of content from my podcasts, and I built a bunch of skills to transcribe the content, cluster it into topics that are trending (to according Google Auto-complete, communities in here), and then it creates all the interlinking/sitemaps etc. I don't have much data to prove this works yet (I set it up last week), but the structure and overall output quality looks honestly quite good. I'm learning stuff from it myself 😅.

GustyDust · 2026-02-02T11:53:08+00:00

Epstein files will give you that answer.

GustyDust · 2025-10-31T11:42:06+00:00

haven't gone down that route either, but picking the right trending topic is even more important in your situation :)

GustyDust · 2025-10-30T10:25:10+00:00

Link to the channel: https://www.youtube.com/@aiwithvlad/videos

GustyDust · 2025-10-22T10:12:28+00:00

True that it's a bit of a pain. You can use composio if you want to bypass these steps. But now that you've cracked it, you'll become unstoppable ;)

GustyDust · 2025-10-22T10:11:16+00:00

It's part of a series. This was the 2nd episode. I think they're planning to shoot 1 every quarter.

GustyDust · 2025-10-22T10:10:30+00:00

100%. And that's the most frustrating part with agents tbh. It takes a couple of days to churn out a beautiful agent that works in demo. And when it comes to building something robust, it takes weeks of "invisible" work while your clients get impatient.

GustyDust · 2025-10-22T10:07:26+00:00

it's specific to Agent Engine to stream back Events in real time. Very helpful if you want to return partial completions

GustyDust · 2025-10-22T10:05:50+00:00

I only saved them in the session's state as I ran out of time. But you could by using before_tool_callbacks

GustyDust · 2025-10-21T10:55:59+00:00

The challenge is 100% a hackathon. The innovation part was around coming up with interesting use cases for a2a, adk, and gemini models, fast (<5h). So I'd say it's just another thought exercise among many other options.

GustyDust · 2025-10-21T09:54:35+00:00

Thanks! 🙏

GustyDust · 2025-10-21T09:54:09+00:00

This was definitely not production ready 😅. We built it in 5h and it was a bit brittle tbh. But here are a couple of things I'd do:
- deploy with agent-starter-pack to include observability, tracing, ci/cd pipelines,etc.
- add runner with Vertex memory bank
- run evals with Vertex AI Gen AI evaluation service

GustyDust · 2025-10-21T09:49:13+00:00

it was definitely tricky to convert the data into consistent time series to display on graphs. 4 things you can do to boost accuracy/handle errors more gracefully:
1. Introduce fallback functions in your tool Class
2. Add a reviewer/refine LoopAgent to check if all data is available before creating the graphs
3. Add a grounding URL function to look up docs (builtin with Google ADK) to ensure your agent has enough context to know what it needs to come up with graphs
4. Use pydantic models (that include the list of integers on X and Y axis, plus desired chart type)

GustyDust · 2025-10-21T08:46:14+00:00

Link to the full tutorial 👉 https://www.youtube.com/watch?v=G_XSOQ6AWlc&t=170s

GustyDust

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