docheinestages

6 ms·

61.
by docheinestages 3mo ago
> Is this really not a mostly “solved” problem using assistive browser (or OS-level) ... > ... performatively sacrifice aesthetics ... One doesn't need to sacrifice aesthetics to make the design legible to an average user. > A
62.
by docheinestages 3mo ago
The landing page doesn't show a link to your GitHub. Despite mentioning "self-hostable", I genuinely thought it was closed-sourced. I doubt first-time users will trust a random tool and grant it database-level access with a c
63.
by docheinestages 3mo ago
I loved the text contrast on the landing page. Grey text on a dark background.
64.
by docheinestages 3mo ago
Any estimates of the performance (prompt processing and decoding tokens/s) on consumer hardware like Macbook Pro M-series?
65.
by docheinestages 3mo ago
That's why I believe the baked weights should represent a generic model that is good at general thinking and tool calling. The rest can be augmented by the OS.
66.
by docheinestages 3mo ago
I got the same vibes. I really wish YCombinator would mentor startups on design. Unfortunately, most of the landing pages in their startup directory look like raw AI designs.
67.
by docheinestages 3mo ago
I got the same impression looking at your website and demo video. They both came across as generic AI-made content.
68.
by docheinestages 3mo ago
I think AI will evolve the same way computers did. We're somewhere in the 80s-90s timeline of the evolution. My prediction is that on-device models will have excellent tool-calling, reasoning, and general skills, but the domain-specifi
69.
by docheinestages 3mo ago
True. It was more of an open letter, with hopes that the Qwen team sees the comments in this thread.
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by docheinestages 3mo ago
Qwen has set an excellent track record for architecting and releasing open-weight models that consumer-grade devices can run. What is needed the most right now is something similar to Bonsai 27B, with a modest memory footprint, but faster a
71.
by docheinestages 3mo ago
> The cloud era already ran this experiment: proprietary APIs plus data gravity made exit punitive. The repatriation wave is the receipt. Some sentences smell a lot like AI.
72.
by docheinestages 3mo ago
Any VC-backed company will eventually pivot.
73.
by docheinestages 3mo ago
I think figuring out if a text is AI-made is a losing battle. What could work is gauging how much effort went into writing the text, regardless of who the author might be. What's easy today is generating mountains of text that are extr
74.
by docheinestages 3mo ago
Oh, I just realized Microsoft removed it a couple weeks ago. I had the link in my bookmarks. The model is available on HuggingFace [1]. [1]: https://huggingface.co/models?sort=trending&search=fastconte...
75.
by docheinestages 3mo ago
Look into FastContext by Microsoft. Not extraordinary but specifically designed for paired usage with a larger LLM to save tokens [1]. [1]: https://github.com/microsoft/fastcontext
76.
by docheinestages 3mo ago
It really depends on which angle you look at it. Is it purely to meet a business goal? Or is it also for personal growth? I think it's a mix of both, but for me it's always important that I engage mentally with the process, learn
77.
by docheinestages 3mo ago
I think the most secure setup, though not so convenient for the average user, is a separate machine with QEMU/KVM. The machine should be isolated adequately, such that even if compromised, it shouldn't be able to cause damage or g
78.
Ask HN: Is GPT-5.5 being nerfed?
1 points by docheinestages 3mo ago|0 comments
79.
by docheinestages 3mo ago
I'm on a 48 GB M5 Macbook Pro. I use 4-bit quants with a context window of 16-32k. I tried Qwen 3.6 27B, but I can only get around 10 tokens per second, but it's painfully slow, and it often fails during `write_file` tool calls, e
80.
by docheinestages 3mo ago
I'm looking for something that runs on an M5 Macbook Pro with 48 GB of unified memory.
81.
by docheinestages 3mo ago
I've been trying various harnesses like Pi, OpenCode, Qwen Code, and Nanocoder. A common problem I keep running into is failed tool calls, regardless of the model. What is the best harness and on-device model combination right now?
82.
by docheinestages 3mo ago
I think this person is exhibiting symptoms of prolonged conversation sessions with LLMs. Maybe this is how they talk/shout/scream at the AI, which has no emotions, and gladly accepts this tone of speech.
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by docheinestages 3mo ago
Do you mind sharing the code?
84.
by docheinestages 3mo ago
I think it's good etiquette to ask the user if they want to download the model or any assets larger than the average page size, in case they have limited bandwidth.
85.
by docheinestages 3mo ago
Every browser should have an embedding API to avoid having to worry about the plumbing and data transfer.
86.
by docheinestages 3mo ago
I think humans don't behave the same as bots when playing this game.
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by docheinestages 3mo ago
Why not! And free hot showers during the summer. Just looking at the progress made since 2023, I don't think the LLM architecture we have today is the most efficient. We need creative game developers to start making LLMs.
88.
by docheinestages 3mo ago
What we really need is a breakthrough in inference or LLM architecture to allow running GLM-5.2-level models at the size of Qwen 3.6 27b or smaller on consumer devices like a 48GB Macbook Pro, and at least at 100 tokens/second. My hypo
89.
by docheinestages 3mo ago
Please enlighten me on how they work. My understanding is that captchas usually either involve challenges for humans or require proof of work (some computation). I found the idea of this game along the lines of a human challenge.
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by docheinestages 3mo ago
I think the developers of Pi made a supply chain mistake by stripping down the core agent and requiring features like subagents to load plugins written by some random person.

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