#source-code-access

#source-code-access

The last week of the release continued the same 'lots of small fixes' trend, but it all really does seem pretty benign, so I've tagged the final 7.0 and pushed it out.

Microsoft did not send me any emails or prior warnings. I have received no explanation for the termination and their message indicates that no appeal is possible. I have tried to contact Microsoft through various channels but I have only received automated replies and bots. I was unable to reach a human.

Gentoo's official migration from Microsoft-owned GitHub to Codeberg is underway, as the Linux distribution fulfills a pledge to ditch the code shack due to "continuous attempts to force Copilot usage for our repositories." The decision was made public last month, when Gentoo confirmed it intended to migrate repository mirrors and pull request contributions to the new home. On February 16, the organization revealed it now had a presence on Codeberg, where contributions could be submitted.

While AI tools are lowering the barrier to development, the gap between speed and manageability is growing. In just over a year and a half, AI code assistants have grown from an experiment to an integral part of modern development environments. They are driving strong productivity growth, but organizations are not keeping up with the associated security and governance issues.

GitHub engineers recently traced user reports of unexpected "Too Many Requests" errors to abuse-mitigation rules that had accidentally remained active long after the incidents that prompted them. According to GitHub, the affected users were not generating high-volume traffic; they were "making a handful of normal requests" that still tripped protections. The investigation found that older incident rules were based on traffic patterns that were strongly associated with abuse at the time, but later began matching some legitimate, logged-out requests.

AI coding tools have caused as many problems as they have solved, according to industry experts. The easy-to-use and accessible nature of AI coding tools has enabled a flood of bad code that threatens to overwhelm projects. Building new features is easier than ever, but maintaining them is just as hard and threatens to further fragment software ecosystems. The result is a more complicated story than simple software abundance.

Software engineering didn't adopt AI agents faster because engineers are more adventurous, or the use case was better. They adopted them more quickly because they already had Git. Long before AI arrived, software development had normalized version control, branching, structured approvals, reproducibility, and diff-based accountability. These weren't conveniences. They were the infrastructure that made collaboration possible. When AI agents appeared, they fit naturally into a discipline that already knew how to absorb change without losing control.