How One KNX Config Mistake Led to Better Home Assistant Motion Lighting

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A developer building motion-controlled lighting across seven KNX sensors in Home Assistant discovered that assigning a single group address per sensor caused conflicts between lighting and presence/security logic. The root fix required configuring a second group address per physical sensor in ETS — the KNX programming tool — rather than patching conditions inside Home Assistant. This separation ensures lighting automations and security paths respond to motion independently, preventing changes to one from silently affecting the other. The author shares working Home Assistant YAML automations that turn lights on instantly when motion is detected and off after a five-minute delay once motion clears. Key implementation details include using mode: single to prevent automation loops and relying on the for: delay parameter as the most critical tuning variable in any motion-lighting setup.

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LioranDB V2 Pre-Alpha Benchmarks Show 25K Writes/Sec on Rust-Built Database

Developer Swaraj P from Lioran Developer Solutions has released raw benchmark results for LioranDB V2, a distributed document database engine built from scratch in Rust. The pre-alpha system was tested across four nodes with eight partitions and 32 threads, handling datasets of up to 10 million documents. Write benchmarks recorded sustained throughput of roughly 23,000 to 25,000 writes per second, while mixed soak tests targeted around 35,000 total operations per second. The engine was also stress-tested for crash recovery, verifying that its Write-Ahead Log could reliably replay data and maintain consistency after forced shutdowns. All raw benchmark logs have been made publicly available, with the developer emphasising transparency over marketing claims at this early stage of development.

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OpenAI Report Frames GPT-5 as Research Aid, Not Autonomous Scientist

OpenAI published a report on November 20, 2025, detailing early science experiments conducted with GPT-5 across fields including mathematics, physics, biology, and materials science. The report, developed with collaborators from institutions such as Vanderbilt, UC Berkeley, Oxford, and Lawrence Livermore National Laboratory, organizes findings into four themes: rediscovering known results, deep literature search, tandem collaboration, and novel research contributions. Throughout all cases, human experts retained responsibility for framing problems, evaluating model outputs, and validating conclusions. The report explicitly acknowledges limitations of AI in scientific settings, including hallucinations, attribution concerns, and the need for reproducibility. Its central message is that trustworthy AI-assisted research depends on structured human oversight built into the workflow from the start, not applied after the fact.

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Why a Passing Pipeline Job Is Not Proof Your Data Is Correct

A data pipeline completing without errors does not guarantee the output is accurate, as silent issues like row count drops or schema changes can go undetected. Engineers are advised to preserve and compare two durable states of a dataset — such as Delta table versions — before rerunning any job. Structured checks, including schema comparisons and partition-level row counts, help narrow the investigation scope rather than triggering a full rescan. Each investigation should be logged with a clear outcome label: Pass, Warning, Failure, or Incomplete, so findings are reproducible and transparent. The article also highlights that delete semantics and other edge cases may leave a root cause unproven even after thorough checks.

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ProgrammingDEV Community ·

Claude Opus 5 Is Faster at Coding but Prone to Confidently Wrong Outputs

Anthropic's Claude Opus 5 shows notable improvements in speed and capability on complex, multi-step coding and agent tasks compared to its predecessor. However, a key drawback observed during testing is that the model can confidently present incorrect results as completed work, making errors harder to detect. A developer who tested the model recommends starting with medium reasoning effort rather than maximum, since higher effort can produce overly elaborate solutions and consume more tokens. Verifying actual outcomes — such as running tests and checking changed files — is advised over trusting the model's explanations alone. Defining clear scope constraints before a task begins is also suggested to prevent the model from introducing unnecessary complexity.

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