Multidict is an implementation of a multidict data structure. From 6.7.0 until 6.9.1, the C extension's items-view reflected union operation, operand | d.items(), in multidict_itemsview_or2_impl and subtraction operation, d.items() - operand, in multidict_itemsview_sub1_impl fail to release new key-identity and value references returned for each operand element. Applications that perform these …

Healthcare startups exchange data with hospitals, practices, payers and EHRs over SFTP, HL7v2, X12 and FHIR APIs. This reference architecture shows how to build one reusable exchange platform on AWS: separate transport from format, normalize clinical data to FHIR R4 with AWS HealthLake, and process every route through eight durable stages using AWS Transfer Family, AWS B2B Data Interchange and Amazon Bedrock-assisted mapping.

> Sigh. AI has it's place without a doubt. It can get you up to speed with a huge codebase (like OpenTTD) much quicker than figuring it all out yourself. It can aggregate data from all over the web and present it in a readable format, much quicker than I could. It can run through your code and highlight issues that a compiler would compile but then cause a crash (nullptr de-reference, double free etc). BUT, it has a narrow view of things. I've had AI reject PR's for an open-source project that I am a team member of, because it doesn't understand or see the bigger picture. Nothing wrong with my code, nothing wrong with the logic, but AI says "no", so the PR just sits there. I don't think any project these days should just reject AI written stuff just because it's AI, or include it for the same reason. Could AI have written the code I did? Absolutely. Did it? No. Did it understand what I had written ? Yes, but only in a narrow context that didn't take into account the broader picture. Did it understand it well enough to approve the PR? No, because it couldn't see the broader picture. AI can be a fantastic tool and allow people with say, less coding experience, to add or alter open source software to produce an outcome they are happy with, but it should always be down to a human to decide whether or not to accept said code. And that shouldn't be based on whether or not 90% of it was AI generated, it should be based on whether the code does what it should, in the way that it should, and follows the coding guidelines of the project involved. If the PR works as intended, has no unintentional side-effects, passes all current and new tests and is well documented, then providing it contributes positively to a project, why would it be a candidate for rejection? AI is a tool at the end of the day, like Lint, or CPPcheck or SonarQube. If the suggestions are right and the code does what it should, without introducing regressions (and on the project I contribute to, that is the biggest bugbear - can't break backwards compatibility!), then why does it matter what tools were used to write it? Don't get me wrong, I have an overwhelming dislike of AI in general, as well as many personal experiences that were negative, but I can still see where and how it can be a positive tool if used and instructed correctly, so maybe, don't dismiss anything that used AI to write large portions of it, out of hand immediately. If it does what it should, when it should, without breaking anything then, why is that, regardless of what or who actually generated the code, a bad thing ? Statistics: Posted by blackeagle — 02 Oct 2026 17:57 * * *

After debate over a 60-day deadline, the School Committee adopted an order (amended to explicitly reference cybersecurity) asking the superintendent to present a district cybersecurity preparedness and continuity readiness assessment; members stressed urgency and the need to protect student data.