We're building an AI computer for consumers, most of whom are quite hesitant with existing services due to privacy concerns and because of costs that add up when using various AI tools.
Stop the environmental damage of data centres by... Making extra hardware and moving inference to a less efficient area? This only makes sense if you're on off-grid solar.
How did they nail down a price but no specs? Very odd.
They're targetting people who want AI and will spend thousands, but don't know enough to ask _any_ details? Why wouldn't that audience just be on chatgpt?
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs:
Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000
Motherboard: custom
Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models:
A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B.
A larger model for harder tasks: gpt-oss-120b.
A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
The chat UI is duplicating the chatgpt/claude web chats, where you've got a list of chats on the left and each new thing you wanna do is expected to be a new chat, and maybe it has memories it writes to or can search chats to cross reference.
I've been using these things a bit differently lately, more of a personal assistant - probably more of the OpenClaw setup I'd guess, though I haven't used OpenClaw. I have my macbook running constantly and it has a session running for reach of my projects, plus one session called pa (personal assistant) which is set up to track tasks and to talk to all the other sessions.
The pa is the only session I directly interact with anymore. It's got it's own Signal account and a single Signal chat is the way I interact with all my sessions. Actually, there are a few sessions run on other machines too that it knows how to get to, but mostly it's just the per-project sessions on my macbook.
I think that's how I'm gonna want stuff like this to work - mostly in one single text thread that delegates to the others. The others don't have to be totally invisible, being able to drop into them individually would be fine as long as it doesn't interfere with the simplicity of the main thread.
So the PA itself isn't really a problem, it mostly just delegates to other sessions and if it has personal assistant-type work to do, it uses subagents. The per-project sessions are also meant to use subagents liberally.
But the actual answer is just one session per project and auto-compacting. Actually I run into usage limits on both codex and claude max subscriptions and so there is also a fair bit of it having to do codex/claude handoffs which is like a worse auto-compact.
I am quite sure I get worse results at a higher cost than I do for my actual day job, where I'm taking ownership of code, getting the actual code I want, and therefore doing a lot more manual context management.
But for side projects and other personal computer usage it's all vibing and auto-compacting.
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs:
Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000
Motherboard: custom
Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models:
A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B.
A larger model for harder tasks: gpt-oss-120b.
A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
Interesting, I've thought of setting up something like this. I would agree with the other comments I want to know more about what is inside the box. Also waiting 12 months for them to start shipping sounds like a long lead time. Is it still a personal computer without a screen and an operating system? How many watts is the Panda?
Just saw your youtube channel, would love to connect (x.com/officialmoezee)
It will have an internal operating system, that can be accessed in debug mode by more technical users, we believe in the right to repair, overclock etc.
I love the idea of this. But things are moving too fast and $20 / month to have a frontier model is something a $3000 non upgradeable black box running an open source model can't compete with.
this is true, but the average consumer is not as concerned about the difference between frontier and where open models are today. We believe that as things move forward open source models are going to become a lot more efficient. The panda is also not limited to one person, a whole household can use panda
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs: Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000 Motherboard: custom Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models: A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B. A larger model for harder tasks: gpt-oss-120b. A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
Pretty cool idea. I'd love to see more about who the team is and how the open source models underneath it work and if they can be swapped out.
Wonder how much RAM
Stop the environmental damage of data centres by... Making extra hardware and moving inference to a less efficient area? This only makes sense if you're on off-grid solar.
Really needs some details on what hardware is in there / what models it can run.
Yeah, I'd rather revamp a Steam machine, for which we know exact specs even pre-launch.
Posted the specs as a standalone comment
Pretty cool! Which models are you running locally? Can users pick/change between models?
right now it's qwen, gpt-oss and ollama
Yup users will be able to switch models
No way I can put any money down without knowing the specs. Unless I am missing something, I didn't see anything on the homepage.
How did they nail down a price but no specs? Very odd.
They're targetting people who want AI and will spend thousands, but don't know enough to ask _any_ details? Why wouldn't that audience just be on chatgpt?
Probably just vaporware at this moment.
Hi, posted the specs above
Hi,
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs: Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000 Motherboard: custom Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models: A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B. A larger model for harder tasks: gpt-oss-120b. A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
hm so it can be reached from anywhere outside, which is scary. It appears to have a speaker or open vent on top but no audio/voice/tts/stt capability?
If I had a device "all AI" for home, stt/tts would be my minimum requirement. Being able to reach it from outside, that's a negative mark for me.
You can only reach it from outside if you choose to set it up that way, by default it's local only.
No speaker, vent.
What justifies the claim of being "the world's first personal AI computer"? There are many computers catering to local AI on the market.
Shipping Dec 2027... I don't know about you, but at the rate of current pace, I don't know what to expect at the end of 2027.
Exactly especially without any hardware specs. This thing could be outdated in early 2027 let alone December.
Dropped the specs in a standalone comment
you don't need to keep replying to everyone with the same comment
there are no notifications for HN and we can all see the multiple long blocks of texts with the same specs existing options provide
The chat UI is duplicating the chatgpt/claude web chats, where you've got a list of chats on the left and each new thing you wanna do is expected to be a new chat, and maybe it has memories it writes to or can search chats to cross reference.
I've been using these things a bit differently lately, more of a personal assistant - probably more of the OpenClaw setup I'd guess, though I haven't used OpenClaw. I have my macbook running constantly and it has a session running for reach of my projects, plus one session called pa (personal assistant) which is set up to track tasks and to talk to all the other sessions.
The pa is the only session I directly interact with anymore. It's got it's own Signal account and a single Signal chat is the way I interact with all my sessions. Actually, there are a few sessions run on other machines too that it knows how to get to, but mostly it's just the per-project sessions on my macbook.
I think that's how I'm gonna want stuff like this to work - mostly in one single text thread that delegates to the others. The others don't have to be totally invisible, being able to drop into them individually would be fine as long as it doesn't interfere with the simplicity of the main thread.
How do you manage context?
Not a gotcha, just trying to understand how your setup works
So the PA itself isn't really a problem, it mostly just delegates to other sessions and if it has personal assistant-type work to do, it uses subagents. The per-project sessions are also meant to use subagents liberally.
But the actual answer is just one session per project and auto-compacting. Actually I run into usage limits on both codex and claude max subscriptions and so there is also a fair bit of it having to do codex/claude handoffs which is like a worse auto-compact.
I am quite sure I get worse results at a higher cost than I do for my actual day job, where I'm taking ownership of code, getting the actual code I want, and therefore doing a lot more manual context management.
But for side projects and other personal computer usage it's all vibing and auto-compacting.
No specs. No examples of models it can run. No idea if any of the source will be available.
Basically vapourware.
Not just vaporware but a scam to collect the $100 deposits and disappear.
December 2027 shipping date is hilarious for an AI hardware product.
Hi,
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs: Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000 Motherboard: custom Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models: A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B. A larger model for harder tasks: gpt-oss-120b. A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
No company name. No contacts.
This has to be a scam for normies to forget about the deposit they made by ship time.
definitely not a scam, we're using feedback on this thread to change the landing page and add more details
This looks like it could be very useful for about 3 months. If you're lucky those 3 months will occur after it launches.
Interesting, I've thought of setting up something like this. I would agree with the other comments I want to know more about what is inside the box. Also waiting 12 months for them to start shipping sounds like a long lead time. Is it still a personal computer without a screen and an operating system? How many watts is the Panda?
Just saw your youtube channel, would love to connect (x.com/officialmoezee)
It will have an internal operating system, that can be accessed in debug mode by more technical users, we believe in the right to repair, overclock etc.
170 W
How much control over the AI does the end user have? Can I direct the AI in any way I choose (ie., tell it do "evil" of some sort)?
You have the control that you have over any open source model
Is this a Magic Jack class thing?
I want this to be a thing, but a $3000 non-upgradable box is not the way we’re going to get there.
nVidia DGX and AMD Strix Point have been out for months already...
Yes but they don't have an out of the box experience for consumers, they're still mostly used by technical folks
https://www.bee-link.com/pages/openclaw
This is what I mean, the non-technical consumer doesn't understand any of that, their understanding of AI stops at ChatGPT, Claude and Gemini.
Some of them might've heard of open claw when it went viral but they definitely don't know what it is or how it works
normies are (were) using openclaw, it's definitely non-technical consumer viable
I love the idea of this. But things are moving too fast and $20 / month to have a frontier model is something a $3000 non upgradeable black box running an open source model can't compete with.
this is true, but the average consumer is not as concerned about the difference between frontier and where open models are today. We believe that as things move forward open source models are going to become a lot more efficient. The panda is also not limited to one person, a whole household can use panda
Hi,
We left the specs out because the landing page was made for non-technical consumers who aren't aware of the hardware required in general.
Here are the specs: Processor (SoC): AMD Ryzen AI Max+ 395 Memory: 128 GB LPDDR5X-8000 Motherboard: custom Storage: NVMe SSD, 4 TB: WD_Black SN850X 4TB
Rest is basic: ethernet, usb c ports, wifi module, power supply
Inference engine: llama.cpp
Models: A fast default for chat, email and calendar: a 30B-class mixture-of-experts model like Qwen3-30B-A3B. A larger model for harder tasks: gpt-oss-120b. A coding model: Qwen3-Coder-Next.
The late release date is specifically to fine tune the experience for the non technical consumer, someone who wouldn't know what model to switch to for what task. We want this to be a seamless out of the box experience. UI/UX is very important here. Another reason for the late release is the chip cant be bought off the shelf and need AMD to sell us the chip at a certain volume
shipping next year with prices from last year, good luck delivering on that price point