Local GLaDOS

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Local GLaDOS

Code is available at: https://github.com/dnhkng/GlaDOS You can also run the Llama-3 8B GGUF, with the LLM, VAD, ASR and TTS models fitting on about 5 Gb of VRAM total, but it's not as good at following the conversation and being interesting. The goals for the project are: All local! No OpenAI or ElevenLabs, this should be fully open source. Minimal latency - You should get a voice response within 500 ms (but no canned responses!) Interruptible - You should be able to interrupt whenever you want, but GLaDOS also has the right to be annoyed if you do... Interactive - GLaDOS should have multi-modality, and be able to proactively initiate conversations (not yet done, but in planning) Lastly, the code base should be small and simple (no PyTorch etc), with minimal layers of abstraction.

Visit product View on Hacker NewsApr 30, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, elevenlabs · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, llama, io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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