Comments URL: https://news.ycombinator.com/item?id=45373466

Points: 3

# Comments: 0

But let's assume you're much better than average at understanding code by reviewing it -- you have another frustrating experience to get through with AI. Pre-AI, let's say 4 days of the week are spend writing new code, while 1 day is spent fixing unforseen issues (perhaps incorrect assumption) that came up after production integration or showing things to real users. Post-AI, someone might be able to write those 4 days worth of code in 1 day, but making decisions about unexpected issues after integration doesn't get compressed -- that still takes 1 day.

So post-AI, your time switches almost entirely from the fun, creative act of writing code to the more frustrating experience of figuring out what's wrong with a lot of code that is almost correct. But you're way ahead -- you've tested your assumptions much faster, but unfortunately that means nearly all of your time will now be spent in a state of feeling dumb and trying to figure out why your assumptions are wrong. If your assumptions were right, you'd just move forward without noticing.

As a SWE at Amazon, I constantly faced two frustrations:

1. Getting interrupted on Slack all day with questions I'd already answered

2. Waiting hours (or days) for responses when I needed information from teammates

When you compare this to the UX of an AI chatbot, humans start to look pretty inconvenient! It’s a bit of a wild take, but it’s really been reflected in my conversations with dozens of engineers, and especially juniors: people would rather spend 20 minutes wrestling with an unreliable AI than risk looking ignorant or wasting their coworkers’ time. One of my early users actually tried the product and told me she’s a bit worried her coworkers would prefer talking to her AI extension over talking to her!

Here’s how it works: It’s a desktop app (mac only right now) that captures screenshots every 5 seconds while you work. It uses a bespoke, ultra-long context vision model (OCR isn’t enough, and generic models are far too expensive!) to understand what you're doing and automatically builds a searchable, hyperlinked knowledge base (wiki) of everything you work on - code you write, bugs you fix, decisions you make, or anything else you do on a computer that could be useful to you or your team’s productivity in the future.

Even if you just turn on Knowledgework for ~30 mins while working on a personal project, I think you’ll find what it produces to be really interesting — something I’ve learned is that we tend to underestimate the extent of the valuable information we produce every day that is just ephemeral and forgotten. There’s also some really great opportunities surrounding quantified self and reflection — just ask it how you could have been more productive yesterday or how you could come across better in your meetings.

The real value comes when your teammates can query your "Extension" - an AI agent that has access to all (only what you choose to share) of your captured work context. Imagine your coworker is on vacation, but you can still ask their Extension: "I'm trying to deploy a new Celery worker. It's gossiping but not receiving tasks. Have you seen this before?"

We’ve spent a great deal of effort on optimizing for privacy as a priority; not just in terms of encryption and data security, but in terms of modulating what your Extension will divulge in a relationship appropriate way, and how you can configure this. By default, nothing is shared. In a team setting, you can choose to share your Extension with particular individuals. You can, in a fine-grained manner, grant and revoke access to portions of your time, or if you are on a tight-knit team, you can just leave it to AI to decide what makes sense to be accessed. This is the area we’re most excited to get feedback on, so we’re really aiming this launch at small, tight knit teams who care about speed and productivity at all costs who use Macs, Slack, Notion, and are all on Claude Code Max plans.

We’re also working on SOC II type 2 compliance and can do on-prem, although on-prem will be quite expensive. If you’re curious about on-prem or additional certifications, I’d love to chat - griffin@knowledgework.ai.

Check it out here: https://knowledgework.ai/

We’ve opened it up today for anyone to install and use for free. If you’re seeing this after Thursday 8/28, we’ll likely have put back the code wall — but we’d be happy to give codes to anyone who reaches out to griffin@knowledgework.ai

Comments URL: https://news.ycombinator.com/item?id=45055510

Points: 8

# Comments: 2

Comments URL: https://news.ycombinator.com/item?id=45055109

Points: 5

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=44474616

Points: 2

# Comments: 0

I find the prospect of an AI continuously monitoring and course correcting very interesting. Like GPS/ maps -- if you go off course, it re-routes you. What will people build with real-time AI like this in the future? I think Cluely may be the best example of this.

I'd personally like a version of the Pavlok device that has a built in camera / mic that allows me to configure these type of triggers with real-time monitoring. It'll probably be a while until we can run an LLM on a watch-sized device in 1-3s but still have ~8hr battery life.

It's 100% browser based -- super simple to try. For those who don't have the device, you can still try it in your browser -- just select "loud beep (through computer)".

It's also open-source: https://github.com/gr-b/screenshockme

Comments URL: https://news.ycombinator.com/item?id=44359194

Points: 3

# Comments: 1

While ChatGPT is a better Google search, Knowledgework is like asking a copy of your own brain for help. Today’s chat interfaces are flawed because they require the user to micromanage: to not only know exactly what can and should be done, but to describe in detail all the specifics required to do it. Knowledgework fixes this with two elements that I believe create a new paradigm for AI enabled software: proactivity and omniscience. Without these two properties, you can’t delegate meaningful real world tasks to AI.

ChatGPT isn’t useful for this. Not because it’s not intelligent enough (it is), but because it lacks the right knowledge. When I speak to knowledge workers about how they use AI for work, they tell me they’d like to delegate more tasks to AI, but that they experience this frustration of needing to micromanage it, re-teaching everything about their project and their team every time.

How does Knowledgework solve omniscience and proactivity to create an AI assistant that’s useful for delegation of real-world work? It’s a desktop vision AI that watches you work, like an intern who is shadowing you, or a pair programmer. It learns the rich, internal (often unwritten) knowledge specific to your projects that's required to contextualize them. It organizes this into neat, understandable documentation of everything you’re doing: a hyperlinked wiki that connects all of your team’s concepts, decisions, definitions, acronyms, tools, etc.

This explicit representation of your knowledge powers the AI assistant to enable useful delegation — but it’s also really useful in it of itself. Curious as to how your team came to a decision on something? Click through the wiki to get context.

The other main feature is the Timeline. It’s kind of like a log or an objective summary of how you spent your time. While the wiki mirrors humans’ associative and conceptual memory, the timeline represents episodic memory. This enables you to visually search through your time: imagine you remember solving a similar problem a few weeks ago, but you don’t quite remember when. By going through the Timeline, you can quickly scan to find the specific work session and ask about what you did.

Together, these representations of your knowledge and experience along with the AI assistant running on top begin to feel like a sort of “digital second brain”. Since I started using it, I’ve had the experience where I’m hesitant to even do things on other devices, because it feels like anything I do there is ephemeral.

If you’re excited to upload your mind and see what the future looks like with this tech, sign up for the waitlist here: https://knowledgework.ai.

Comments URL: https://news.ycombinator.com/item?id=44117360

Points: 3

# Comments: 1

Humans require a lot of back and forth effort for "alignment" with regular "syncs" and "iterations" and "I'll get that to you by EOD". If you approach the potential of natural interfaces with expectations that frame them the same way as 2000s era software, you'll fail to be creative about new ways humans interact with these systems in the future.

The other great thing about this mode is that it can double as a teaching methodology. If I have a complicated interface that is not very discoverable, it may be hard to sell potential users on the time investment required to learn everything. Why would I want to invest hours into learning non-transferrable knowledge when I'm not even sure I want to go with this option versus a competitor? It will be a far better experience if I can first vibe-use the product , and if it's right for me, I'll probably be incented to learn the inner workings of it as I try to do more and more.

I wrote a quick script that calculates facial landmarks on your webcam feed in realtime and plays a sound if you fail to blink for a configurable number of seconds.

I use the common way of detecting a blink, which is to calculate the ratio between the height and width of the visible portion of the eye. This can be done with just 6 landmarks -- two on the upper eyelid, two on the lower eyelid, and one on either side of the eye. I found that this doesn't work well in all angles / postural positions, so I also augment this with another metric that corrects for head tilt. It's not perfect (if you tilt your head such that your ear is brought closer to your shoulder, it may over-detect blinks) but it's extremely good in normal scenarios where I don't knot myself into a pretzel.

It runs significantly faster than realtime on my M2, and I leave it on all day when I work from home.

Comments URL: https://news.ycombinator.com/item?id=43472919

Points: 4

# Comments: 0

In other words, just because we know that ultimately (possibly in 20+ years) the Tesla compute-only approach will be simpler and more effective, Tesla might not survive to see this happen. Instead, manual feature engineering and hacking can always give temporary gains over data and compute driven approaches. The bitter lesson was clear about this. I suspect Waymo will win, and at some point in the future once they are out of their growth at all costs stage, they will transition into their maximum value extraction stage, in which vision will make significantly more economic sense than LiDAR. But once they win, they’ll have plenty of time to see the bitter lesson through its ultimate consequences. Elon is right, but he’s probably too early.

Exactly! What a perfect formulation of the problem.

Comments URL: https://news.ycombinator.com/item?id=42215088

Points: 2

# Comments: 0