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Go 1.22, SQLite, and Next.js: The "Boring" Back End

https://mohammedeabdelaziz.github.io/articles/go-next-pt-2
1•mohammede•25s ago•0 comments

Laibach the Whistleblowers [video]

https://www.youtube.com/watch?v=c6Mx2mxpaCY
1•KnuthIsGod•1m ago•0 comments

I replaced the front page with AI slop and honestly it's an improvement

https://slop-news.pages.dev/slop-news
1•keepamovin•6m ago•1 comments

Economists vs. Technologists on AI

https://ideasindevelopment.substack.com/p/economists-vs-technologists-on-ai
1•econlmics•8m ago•0 comments

Life at the Edge

https://asadk.com/p/edge
1•tosh•14m ago•0 comments

RISC-V Vector Primer

https://github.com/simplex-micro/riscv-vector-primer/blob/main/index.md
2•oxxoxoxooo•17m ago•1 comments

Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

2•InvoxoEU•18m ago•0 comments

A Tale of Two Standards, POSIX and Win32 (2005)

https://www.samba.org/samba/news/articles/low_point/tale_two_stds_os2.html
2•goranmoomin•22m ago•0 comments

Ask HN: Is the Downfall of SaaS Started?

3•throwaw12•23m ago•0 comments

Flirt: The Native Backend

https://blog.buenzli.dev/flirt-native-backend/
2•senekor•24m ago•0 comments

OpenAI's Latest Platform Targets Enterprise Customers

https://aibusiness.com/agentic-ai/openai-s-latest-platform-targets-enterprise-customers
1•myk-e•27m ago•0 comments

Goldman Sachs taps Anthropic's Claude to automate accounting, compliance roles

https://www.cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
2•myk-e•29m ago•4 comments

Ai.com bought by Crypto.com founder for $70M in biggest-ever website name deal

https://www.ft.com/content/83488628-8dfd-4060-a7b0-71b1bb012785
1•1vuio0pswjnm7•30m ago•1 comments

Big Tech's AI Push Is Costing More Than the Moon Landing

https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046
3•1vuio0pswjnm7•32m ago•0 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
2•1vuio0pswjnm7•34m ago•0 comments

Suno, AI Music, and the Bad Future [video]

https://www.youtube.com/watch?v=U8dcFhF0Dlk
1•askl•36m ago•2 comments

Ask HN: How are researchers using AlphaFold in 2026?

1•jocho12•39m ago•0 comments

Running the "Reflections on Trusting Trust" Compiler

https://spawn-queue.acm.org/doi/10.1145/3786614
1•devooops•44m ago•0 comments

Watermark API – $0.01/image, 10x cheaper than Cloudinary

https://api-production-caa8.up.railway.app/docs
1•lembergs•45m ago•1 comments

Now send your marketing campaigns directly from ChatGPT

https://www.mail-o-mail.com/
1•avallark•49m ago•1 comments

Queueing Theory v2: DORA metrics, queue-of-queues, chi-alpha-beta-sigma notation

https://github.com/joelparkerhenderson/queueing-theory
1•jph•1h ago•0 comments

Show HN: Hibana – choreography-first protocol safety for Rust

https://hibanaworks.dev/
5•o8vm•1h ago•1 comments

Haniri: A live autonomous world where AI agents survive or collapse

https://www.haniri.com
1•donangrey•1h ago•1 comments

GPT-5.3-Codex System Card [pdf]

https://cdn.openai.com/pdf/23eca107-a9b1-4d2c-b156-7deb4fbc697c/GPT-5-3-Codex-System-Card-02.pdf
1•tosh•1h ago•0 comments

Atlas: Manage your database schema as code

https://github.com/ariga/atlas
1•quectophoton•1h ago•0 comments

Geist Pixel

https://vercel.com/blog/introducing-geist-pixel
2•helloplanets•1h ago•0 comments

Show HN: MCP to get latest dependency package and tool versions

https://github.com/MShekow/package-version-check-mcp
1•mshekow•1h ago•0 comments

The better you get at something, the harder it becomes to do

https://seekingtrust.substack.com/p/improving-at-writing-made-me-almost
2•FinnLobsien•1h ago•0 comments

Show HN: WP Float – Archive WordPress blogs to free static hosting

https://wpfloat.netlify.app/
1•zizoulegrande•1h ago•0 comments

Show HN: I Hacked My Family's Meal Planning with an App

https://mealjar.app
1•melvinzammit•1h ago•0 comments
Open in hackernews

Show HN: Data from a mixed-brand LiFePO₄ battery bank

4•wkcollis1•3w ago
Hi HN — I’m sharing an empirical, long-term dataset from a DIY energy-storage project that ended up testing a common assumption in battery design.

Conventional advice says never mix battery brands. That guidance is well-founded for series strings, but there’s surprisingly little data on purely parallel configurations.

I built a 12 V, 500 Ah LiFePO₄ battery bank (1S5P) using mixed-brand cells and instrumented it for continuous monitoring over 73+ days, including high-frequency voltage sampling. The goal was to see whether cell-level differences actually manifest over time in a parallel topology.

What the data shows

No progressive voltage divergence across the observation period

Voltage spread remained within ~10–15 mV

Measured Peukert exponent ≈ 1.00

Thermal effects were small relative to instrumentation noise

In practice, the parallel architecture appears to force electrical convergence when interconnect resistance is low. I’ve been referring to this as “architectural immunity” — the idea that topology can dominate cell-level mismatch under specific conditions.

This is not a recommendation to mix batteries casually, and it’s not a safety guarantee. It’s an attempt to replace folklore with measurements and to define the boundary conditions where this does or does not hold.

Everything is public:

Raw CSV data

Analysis scripts

Full PDF report

Replication protocol

Repo: https://github.com/wkcollis1-eng/Lifepo4-Battery-Banks

I’m posting this to invite critique — especially around failure modes, instrumentation limits, or cases where this model would break down (e.g., higher C-rates, aging asymmetry, thermal gradients, different chemistries).

Happy to answer technical questions.

Comments

theamk•3w ago
By "voltage spread", did you mean "difference in voltage between each battery"? Can you clarify how did you calculate this? I looked at the report but could not find the details nor raw data.

(It is easy to calculate in series packs, but the parallel ones would be tricky, since the bus links will equalizes the voltage. Did you manually remove the links and then measures each battery's voltage? Or did you estimate spread by measuring voltage drop between the bus?)

wkcollis1•3w ago
Yes — good question. In this study “voltage spread” does not mean per‑battery terminal differences. I did *not* disconnect the packs or probe each unit individually.

Because the cells are hard‑paralleled, their terminals are forced to the same potential, so true inter‑battery divergence can’t be measured without isolation taps. Instead, “spread” refers to:

*Voltage_Max – Voltage_Min of the pack‑level voltage within each hourly window.*

This captures short‑term variation in the measured pack voltage (ADC noise, EMI artifacts, inverter mode shifts, temperature coefficient), not cell‑to‑cell imbalance.

The raw data is in `Data/combined_output.csv` with columns:

``` Timestamp, Voltage_Min, Voltage_Max ```

Those come from 60‑second samples aggregated hourly. The analysis scripts compute:

``` Spread = Voltage_Max – Voltage_Min ```

So the ~10–15 mV “spread” in the report reflects the measurement envelope of the pack, not divergence between individual batteries. Measuring true per‑battery drift would require either per‑cell taps or momentary isolation, which wasn’t part of this study.

Happy to go deeper if you want details on sampling or noise characterization.