New tool calculates how long a local LLM setup takes to pay for itself

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A developer has built a web tool called 'Sunk Cost' to help users determine the break-even point of running large language models on local hardware versus paying for cloud-based API access. The tool takes inputs such as the machine type, the model being used, and daily token consumption to produce a payback timeline. It was created in response to common claims that buying personal hardware to run AI models locally is ultimately cheaper than renting compute by the token. The project focuses purely on cost savings and does not account for other reasons someone might prefer local inference, such as privacy or offline access. The developer has shared it on Hacker News and is actively seeking feedback to improve its usefulness.

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Developer Fine-Tunes Open LLMs to Support Moroccan Arabic Darija and Arabizi

A developer has released two open-source language models, SILMA-9B-Darija and SILMA-2B-Darija, specifically fine-tuned to handle Moroccan Arabic (Darija) and Arabizi, the Latin-script form of Arabic that uses numbers for certain sounds. The models were trained on a dataset of over 46,000 cleaned Darija instruction and conversation pairs drawn from real Moroccan exchanges and cultural content. Benchmarks run on a 100-question test covering local history, proverbs, and code-switching showed both models outperforming existing Moroccan-dialect alternatives on accuracy and Darija fluency. Unlike standard Arabic models that default to Gulf Arabic or Modern Standard Arabic, the new models retain native Darija vocabulary and handle prompts in both Arabic script and Latin letters. Both models are publicly available on Hugging Face, with the 9B version requiring around 6 GB VRAM and the 2B version running on under 3 GB, making it suitable for consumer devices.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

30-Day AI Self-Review Experiment Missed a Bug a Human Caught in 5 Minutes

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