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A code injection bug in FS-QUO and an insecure deserialization flaw in NetWeaver could lead to arbitrary code execution.

The post SAP Patches Critical FS-QUO, NetWeaver Vulnerabilities appeared first on SecurityWeek.

The telecommunications equipment and services giant has blamed the incident on a third-party vendor.

The post Thousands Affected by Ericsson Data Breach appeared first on SecurityWeek.

Despite the predictions that SIEM is going away, its role is actually evolving. Learn why SIEM tools remain essential to enterprise security operations.

London Mayor Sadiq Khan reveals in a scrutiny session with London Assembly members that the Met is set to trial a facial-recognition phone app for officers

Article URL: https://sephnewman.substack.com/p/offlining-a-live-game-with-net-native

Comments URL: https://news.ycombinator.com/item?id=47323396

Points: 1

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Article URL: https://economist.com/china/2026/03/05/china-sets-its-lowest-growth-target-for-a-generation

Comments URL: https://news.ycombinator.com/item?id=47323387

Points: 1

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Article URL: https://www.nber.org/papers/w34866

Comments URL: https://news.ycombinator.com/item?id=47323381

Points: 1

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Article URL: https://www.brookings.edu/articles/building-pro-worker-ai/

Comments URL: https://news.ycombinator.com/item?id=47323379

Points: 1

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Article URL: https://arxiv.org/abs/2603.06731

Comments URL: https://news.ycombinator.com/item?id=47323378

Points: 1

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Article URL: https://another.rodeo/straight-talk-ic-path/

Comments URL: https://news.ycombinator.com/item?id=47323355

Points: 1

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Article URL: https://jotalea.com.ar/read.php?entry=KBojOqSWHyZPMYCf

Comments URL: https://news.ycombinator.com/item?id=47323335

Points: 1

# Comments: 0

I’m working on a broker import pipeline for retail portfolios, and I underestimated how messy exports are in practice.

Problems I’ve seen: - CSV, JSON and PDF exports for the same broker - EU/US number formats - date-first vs month-first ambiguity - ISIN/ticker/name mismatches - duplicate rows and partial positions - bad parses silently corrupting cost basis

My current approach is deterministic-first: 1. parse structured exports locally 2. only use an LLM fallback when parsing fails 3. normalize symbols and reject invalid rows 4. require human review before persistence 5. apply imports conservatively to avoid cost-basis drift

I’m trying to think clearly about the invariants this system should enforce.

For those who’ve worked on financial imports, accounting systems, or safety-critical data pipelines: - what invariants would you absolutely enforce? - where would you draw the boundary between deterministic logic and LLM extraction? - what would you log for replay/debug/auditability?

Happy to share implementation details if useful.

Comments URL: https://news.ycombinator.com/item?id=47323321

Points: 2

# Comments: 0

Article URL: https://www.npr.org/2026/03/06/nx-s1-5732793/college-student-perspective-using-ai-in-class

Comments URL: https://news.ycombinator.com/item?id=47323320

Points: 2

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Article URL: https://blog.adafruit.com/2026/03/09/disassemble-z80-instructions-by-changing-the-font/

Comments URL: https://news.ycombinator.com/item?id=47323318

Points: 2

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Article URL: https://github.com/getnao/prompt-mentions

Comments URL: https://news.ycombinator.com/item?id=47323304

Points: 2

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Article URL: https://msspproviders.io/

Comments URL: https://news.ycombinator.com/item?id=47323300

Points: 2

# Comments: 1

Article URL: https://www.darpa.mil/research/programs/explainable-artificial-intelligence

Comments URL: https://news.ycombinator.com/item?id=47323293

Points: 2

# Comments: 0

We keep shipping data to the cloud to analyze it, but modern Macs have enough horsepower to query millions of rows locally in milliseconds. Even an 8GB MacBook Neo.

I built ColumnLens because I wanted to stop uploading sensitive data to SaaS tools just to run a GROUP BY. It's a native C++ desktop app that opens 5GB+ CSV, JSONL, Parquet, and Excel files in about 3 seconds, then lets you query them with DuckDB's full SQL engine — JOINs, CTEs, window functions, all running on your machine.

The idea is simple: your laptop is a perfectly good analytics workstation. You shouldn't need a cloud pipeline to look at your data.

Some things I learned building it:

- DuckDB's columnar engine is absurdly efficient. 12M rows, sub-second queries, under 1 GB RAM. On hardware that fits in a backpack.

- ImGui + OpenGL is a great stack for data-heavy desktop apps. No DOM, no layout engine overhead — just raw GPU rendering. The data grid scrolls 12M rows without a single frame drop.

- I added a "3D City View" that maps rows to buildings (height = one column, color = another). Sounds gimmicky, but outliers and clusters are immediately visible in ways they aren't in tables or charts. Our brains are good at spotting the tall building in a skyline.

- Lua scripting turned out more useful than expected. People write small scripts to fetch data from APIs, run queries, and configure charts — repeatable analysis without leaving the app.

- Everything runs locally. Zero telemetry, zero network calls. The binary is 33MB, statically linked.

Free download at https://columnlens.com

Would love to hear from anyone else building local-first data tools — I think the "ship everything to the cloud" era is starting to swing back.

Comments URL: https://news.ycombinator.com/item?id=47323284

Points: 2

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Article URL: https://www.unclaimedbaggage.com/pages/2026-found-report

Comments URL: https://news.ycombinator.com/item?id=47323256

Points: 2

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