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Zero dependency LLM standby, rollouts, no proxy

Hacker News

Zero dependency LLM standby, rollouts, no proxy

We believe you should not rely on any company for your critical LLM infra. This does not proxy via another service that might go down or be beholden to whims of some board. We wrote logos-shift for our own needs and are open sourcing it. What it does: - Save input/output data, no proxy, no slowdowns, double buffering, separate thread - Finetune your own model, or we can for a fee. - Auto rollout your model when ready. We recommend keeping your LLM at 80/20 for most cases and rollout to 100 on failure. How to use it: pip install logos_shift_client from logos_shift_client import LogosShift # Initialize with your API key (without if you just want the local copy) logos_shift = LogosShift(api_key="YOUR_API_KEY") # Instrument your function @logos_shift() def add(x, y): return x + y result = add(1, 2) # Optionally, provide feedback logos_shift.provide_feedback(result['bohita_logos_shift_id_123', "success") We can manage all this for you of course, but its not hard to do it yourself. Save data on our infra for free for upto 1GB. $10 per month for after. Download your data anytime. No lock in. Feel free to ask questions and how can we make this easier for you.

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: month, para · Missing: mobile apps, ios, personal
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, open · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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