Tech Feed - February 10, 2026

Feb 10, 2026

Articles and podcasts from the software engineering world.

Software Engineering Daily

Python 3.14 with Łukasz Langa

Duration: 47 min

Here is a comprehensive summary of the key points from this podcast episode on Python 3.14:

Opening Context:

The guest is Łukasz Langa, the CPython Developer in Residence at the Python Software Foundation.
The main focus is on the new features and improvements in the Python 3.14 release.

Key Discussion Points and Insights:

Python 3.14 formally supports free-threaded no-GIL mode, allowing Python to scale better on multi-core systems. This is a significant long-term improvement, though not yet the default.
The new sub-interpreter support provides isolation between different Python environments running in the same process, useful for applications like digital audio workstations.
Template string literals (t-strings) provide a new syntax for creating template objects, enabling more structured string handling for use cases like HTML, SQL, etc.
Deferred evaluation of type annotations solves issues with forward references, making type hints more flexible and usable, especially for runtime type introspection.
The podcast discusses Python's evolution, the balance between new features and backwards compatibility, and the deprecation process for older functionality.

Notable Technologies, Tools, and Concepts:

Free-threading and the Global Interpreter Lock (GIL)
Sub-interpreters and the concurrent.interpreters module
Template string literals (t-strings)
Deferred evaluation of type annotations
Deprecation policies and the transition between Python versions

Practical Implications and Recommendations:

Developers should test the free-threaded Python 3.14t interpreter if their applications can benefit from better scaling on multi-core systems.
Library maintainers are encouraged to prepare their packages for the transition to free-threading and new type annotation features.
Users should be aware of the ongoing deprecation process and transition timelines when relying on older Python features.
The new debugging and profiling tools, including the asyncio-specific features, can improve visibility and debugging of complex Python applications.

Overall, this episode provides a comprehensive overview of the major new features in Python 3.14, their technical details, and the broader context of Python's evolution and the tradeoffs involved in introducing new functionality while maintaining backwards compatibility.

Dev Interrupted

Multi-agent orchestration in Slack | Saleforce's Kurtis Kemple

Duration: 34 min | Read Transcript

Here is a comprehensive summary of the podcast episode "Multi-agent orchestration in Slack | Salesforce's Kurtis Kemple":

Opening context: The guest is Kurtis Kemple, the Senior Director of Developer Relations at Slack. The main topic of discussion is how Slack is evolving to become a platform where work is not just discussed, but actually done, through the integration of various AI-powered agents and tools.

Key discussion points and insights:

Slack has been focused on enabling agent-based workflows, starting with integrations like Salesforce's Agent Force. This has led Slack to rethink how to build a platform that supports any kind of agentic workflow in a structured, consistent way.
Context is critical for these AI-powered agents to perform well. Slack conversations and channels provide a rich source of contextual information that can be leveraged by these agents.
The concept of "leaky prompts" - when users provide imperfect prompts to AI models, leading to conversations going off-track. Slack aims to address this by providing ways for developers to build contextual representations to keep conversations on task.
Slack is enabling developers to build conversational AI experiences that integrate directly into the Slack platform, rather than building separate apps. This allows for seamless handoffs between humans and agents.
Examples discussed include using AI to triage sales pipeline tasks, automatically generate Figma designs, and manage workspace cleanup and organization.

Notable technologies, tools, or concepts mentioned:

Slack Bolt framework and CLI for building Slack apps and agents
Workflow Builder for creating no-code automations in Slack
Integrations with AI platforms like Anthropic, OpenAI, and Vercel
Concepts like "context engineering", "agentic workflows", and "multi-agent orchestration"

Practical implications or recommendations:

Slack is positioning itself as an "agentic operating system" where humans and AI agents can collaborate seamlessly, with Slack providing the integration layer and contextual data.
Developers are encouraged to build "conversations" rather than standalone apps, taking advantage of Slack's multi-user, multi-turn interface.
Slack provides tools and guidance to help developers quickly build, deploy, and iterate on AI-powered experiences within the Slack platform.
For non-technical users, Slack aims to enable more self-service automation and AI assistance through features like Workflow Builder.
Recommendations for getting started with AI in Slack include identifying time-consuming manual tasks, starting small, and iterating based on quick wins.

Overall, the episode paints a vision of Slack evolving into a central hub where humans and AI agents can seamlessly collaborate, with Slack providing the infrastructure and contextual data to enable these new ways of working.