Show notes
Topics covered in this episode:
OpenAI's Python SDK has migrated to HTTPX2
TMOG - Native Task Manager for macOS, Windows, and Linux
wrapture - one wrapper for mocking, tracing, and observability
linkedin2md: turn your LinkedIn export into 40+ Markdown files
Extras
Joke
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Calvin #1: OpenAI's Python SDK has migrated to HTTPX2
The OpenAI Python SDK has migrated to HTTPX2, the Pydantic-stewarded fork of httpx.
Pydantic picked it up citing "limited activity recently" in the original project, promising "a reliably maintained path forward."
If you just use the default client, nothing to do.
No code changes.
The catch is TLS.
Quoting the guide: HTTPX "previously verified certificates against the CA bundle provided by certifi.
HTTPX2 instead uses the operating-system trust store, and the SDK no longer installs certifi."
That "can break certificate verification in minimal container images without system CA certificates, environments using corporate TLS-inspecting proxies, and deployments that relied on a custom or modified certifi bundle."
The fix is SSL_CERT_FILE or SSL_CERT_DIR, or pass your own ssl.SSLContext via verify.
Deeper integrations need real edits: custom clients, auth handlers, hooks, and request mocking all take HTTPX2 objects now, and plain httpx is no longer pulled in transitively.
So import httpx in your own code means declaring it yourself or moving over.
Temporary escape hatch: a legacy HTTPX client
Michael #2: TMOG - Native Task Manager for macOS, Windows, and Linux
A native, deeply instrumented system monitor for macOS, Windows, and Linux, now in public beta - from Plummers' Software, i.e. Dave Plummer, who wrote the original Windows Task Manager and donated it to Microsoft in 1995.
Wikipedia
Three real native apps: Swift/AppKit on macOS, Win32 on Windows, C++/Qt 6 on Linux, with a shared C++ core keeping metric semantics aligned - no browser shell anywhere.
One dense summary: CPU, clocks, thermals, GPU, memory, storage, network, energy, and the processes responsible for the load, all click-through.
Per-core honesty: logical processor and NUMA views, P and E cores color-coded, optional kernel time, 60 FPS live meters.
Memory with context: pressure, wired, compressed, cached, committed, available, and swap, plus configurable scrolling history.
Processes that act like processes: tree view, filtering, sorting, follow mode, and native verbs including service and launchd control.
Phosphor themes: light, dark, green, amber, blue, or mono, with color and saturation you tune yourself.
Calvin #3: wrapture - one wrapper for mocking, tracing, and observability
Graham Dumpleton, author of wrapt and the original New Relic Python agent, has released wrapture.
The name is wrapt plus capture.
The core idea: wrap real code instead of replacing it, so the real code still runs while you watch every call.
Name a method with wrapture.binding(Class, "method"), open a timeline(), and you get a tape of what actually happened.
Real return values, real nesting, arguments normalised against real signatures.
tape.tree() prints the call graph as it ran.
One mechanism, three jobs: monkey patching with a real lifecycle (apply, remove, suspend, plus returns, raises, transforms_args), unit testing that asserts on real call flow instead of a flat MagicMock call list, and ad-hoc tracing of a running app.
The testing pitch is error paths.
Inject TimeoutError at the payment gateway, then assert the ledger was never written.
Stubs and mocks are strict and spec-required, and there is deliberately no bare Mock().
Tracing needs no code at all.
A wrapture.toml naming targets and a sink, run with python -m wrapture main.py, and you get a live call tree with timings.
It captures ordinary logging calls as nested events, and with the otel extra it exports spans, metrics and correlated logs with W3C trace ids that join across services.
Every line of code and docs was AI-written under their direction, and they say so up front.
Two weeks from first commit, eleventh alpha, over 1000 tests, 150+ pages of docs.
Alpha on PyPI, needs Python 3.12+ and wrapt 2.4.0+.
Michael #4: linkedin2md: turn your LinkedIn export into 40+ Markdown files
Via Juan Manuel Daza - a Python CLI that unpacks LinkedIn's data-export ZIP into clean, per-category Markdown you can drop straight into an LLM.
One command: linkedin2md Complete_LinkedInDataExport.zip, plus o for output dir, -lang en|es, and -pdf.
40+ output files: profile, experience, education, skills, connections, posts, comments, reactions, recommendations, endorsements, job applications, even ad targeting and LinkedIn's inferences about you.
Built for LLM analysis: the README pitches NotebookLM, Claude Projects, Obsidian, and Ollama, with example prompts like "what patterns do you see in my career transitions?"
PDF resume mode: -pdf renders an A4 CV via weasyprint, and degrades gracefully to Markdown-only if it isn't installed.
Dependency note: "pure Python / zero-dep" holds for the Markdown path only - the PDF path needs weasyprint and markdown installed.
Install: pipx install linkedin2md recommended, pip in a venv otherwise - 86% Python, 10 releases, v0.3.1 in May.
Agentic dev angle: repo ships opencode config and an N3RV subagent pipeline, including a "judgment day" dual-model adversarial PR review.
Extras
Calvin:
EVE Online Migrates to Python 3
Michael:
Dinkus by Will McGugan
Joke: Tao of Programming: Book 5 Maintenance