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Topics covered in this episode:

• GreyNoise IP Check
• tprof: a targeting profiler
• TOAD is out
• Extras
• Joke

Watch on YouTube

About the show

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• Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)
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Michael #1: GreyNoise IP Check

• GreyNoise watches the internet's background radiation—the constant storm of scanners, bots, and probes hitting every IP address on Earth.
• Is your computer sending out bot or other bad-actor traffic? What about the myriad of devices and IoT things on your local IP?
• Heads up: If your IP has recently changed, it might not be you (false positive).

Brian #2: tprof: a targeting profiler

• Adam Johnson
• Intro blog post: Python: introducing tprof, a targeting profiler

Michael #3: TOAD is out

• Toad is a unified experience for AI in the terminal
• Front-end for AI tools such as OpenHands, Claude Code, Gemini CLI, and many more.
• Better TUI experience (e.g. @ for file context uses fuzzy search and dropdowns)
• Better prompt input (mouse, keyboard, even colored code and markdown blocks)
• Terminal within terminals (for TUI support)

Brian #4: FastAPI adds Contribution Guidelines around AI usage

• Docs commit: Add contribution instructions about LLM generated code and comments and automated tools for PRs
• Docs section: Development - Contributing : Automated Code and AI
• Great inspiration and example of how to deal with this for popular open source projects
  • “If the human effort put in a PR, e.g. writing LLM prompts, is less than the effort we would need to put to review it, please don't submit the PR.”
• With sections on
  • Closing Automated and AI PRs
  • Human Effort Denial of Service
  • Use Tools Wisely

Extras

Brian:

• Apparently Digg is back and there’s a Python Community there
• Why light-weight websites may one day save your life - Marijke LuttekesHome

Michael:

• Blog posts about Talk Python AI Integrations
  • Announcing Talk Python AI Integrations on Talk Python’s Blog
  • Blocking AI crawlers might be a bad idea on Michael’s Blog
• Already using the compile flag for faster app startup on the containers:
  • RUN --mount=type=cache,target=/root/.cache uv pip install --compile-bytecode --python /venv/bin/python
  • I think it’s speeding startup by about 1s / container.
• Biggest prompt yet? 72 pages, 11, 000

Joke: A date

• via From Pat Decker

Download audio: https://pythonbytes.fm/episodes/download/467/toads-in-my-ai.mp3

Most teams don’t hit scaling challenges with Azure AI Foundry on day one.

Early on, things are simple. One or two applications call Foundry directly. Traffic is predictable. Model experimentation moves fast. Everything works, and there’s no reason to add extra layers.

Then adoption grows. More applications start calling the same models. Traffic becomes spiky. Teams want better visibility into usage. Questions about rate limits, authentication, and how to evolve models over time begin to surface.

This is usually the moment when teams start asking: “Do we need some kind of control layer in front of Foundry?”

The signals that start to show up

Across many startups, the same patterns tend to emerge as Foundry usage scales:

• Multiple clients and services calling the same Foundry endpoints
• The need for consistent rate limiting and access control
• A desire to evolve models or deployments without touching every client
• Limited visibility into who is calling what, and how often

None of these are problems at small scale. But together, they create friction as usage grows.

A pattern we often see working well

A common pattern at this stage is placing a gateway in front of Azure AI Foundry APIs.

Client applications call a single gateway endpoint, where policies such as authentication, rate limits, and routing are applied before requests are forwarded to Foundry model deployments.

Rather than having every application talk directly to Foundry, teams introduce a control layer that sits between clients and Foundry. On Azure, this is often implemented using API Management with GenAI capabilities.

This gateway does not replace Foundry. Foundry remains the model and AI platform. The gateway simply becomes the entry point for client traffic.

What this enables in practice

When teams introduce a gateway layer, a few things become much easier:

• A single, stable API surface for applications, even as models or deployments evolve
• Centralized throttling and authentication, instead of per-client logic
• Policy-based routing across models or backends without changing clients
• Improved request-level observability into usage patterns, latency, and errors

Importantly, this structure lets teams scale without slowing down experimentation. Model teams can continue to iterate, while platform concerns stay centralized.

What this pattern is not

It’s worth calling out what this approach is not:

• It’s not required on day one
• It’s not mandatory for every startup
• It’s not about adding complexity early

Many teams run successfully without a gateway for a long time. This pattern becomes useful when scale, team size, or operational needs make direct integrations harder to manage.

When teams usually consider this

From experience, teams tend to explore this pattern when:

• Foundry usage spans multiple applications or teams
• Rate limits and quotas need consistent enforcement
• There’s a desire to future-proof model or deployment changes
• Observability and governance start to matter more

If those conversations are already happening, it’s often a good time to look at a gateway approach.

How this looks on Azure

On Azure, this pattern is commonly implemented using:

• Azure API Management as the gateway
• AI-aware policies for rate limiting, routing, and governance
• Azure AI Foundry as the backend model platform

The architecture stays flexible. Teams can start simple and add capabilities over time as needs evolve.

Closing thoughts

This pattern is less about tooling and more about timing.

Adding a gateway too early can slow teams down. Adding it too late can make change painful. The right moment is usually when Foundry usage starts to feel like a shared platform rather than a single experiment.

For teams approaching that stage, a gateway can provide structure without taking away speed.

References

• Enable API Management gateway for Azure AI Foundry
• GenAI gateway capabilities in API Management
• Gateway patterns for multi-backend AI setups
  
  

This blog post was created with the help of AI tools. Yes, I used a bit of magic from language models to organize my thoughts and automate the boring parts, but the geeky fun and the in C# are 100% mine.

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