Toni Melisma

AI, Voice and Mobile Product Manager @Salesforce. In SF, originally from Finland. @KPMG alumni

[Slack @SlackHQ]

Dear Claude from Finance,

Your @mentions are about to go way up.

We’re so sorry!

[Toni Melisma @tonimelisma]

@UrbanCourtyard One important correction (having lived in Helsinki): people don't have to take care of it! The HOA does that. No yard work. Big difference to a single family house. You just enjoy it 😀

[Dwarkesh Patel @dwarkesh_sp]

My family moved to the US when I was 8, but by the time I turned 20, my dad was still on an H1B (waiting to get processed for a green card).

Once I turned 21, I would age out as his dependent, despite the fact that I basically grew up in the US.

I thought I'd have to become a code monkey after college, and even that only if I was lucky enough to win the H1B lottery.

Otherwise, back to India.

I had become a huge fan of @paulg's essays in college. I was actually depressed that my desire to start a startup or do something entrepreneurial was basically hopeless.

Working on the promising podcast I was doing as a side project? A beyond impossible pipe dream.

Even after 9 years, my dad wasn't able to get a green card - and the lines were only getting longer over time. I figured I'd be an old man before I could quit some FANG job and build my own thing.

By some miracle, COVID travel restrictions cleared out the lines, and I got my green card literally months before I would have aged out.

If not for this unbelievable coincidence, I would not be hosting the podcast.

In the best case, I would be shifting pixels around in the 3rd sub-sub-menu of some big tech software.

I'm incredibly grateful I made it through.

But it's unconscionable that we put the kids of high skilled immigrants through all this anxiety, and in many cases make them repeat the nerve-racking indentured life trajectory that they had to watch their parents go through.

[Toni Melisma @tonimelisma]

@patio11 @moseskagan In Helsinki we allow all residents, whether citizens or not, to vote in city elections

[Marc Randolph @marcrandolph]

You don’t need a better idea. You don’t need more funding. You don’t need to wait until it’s ready.

You just need to be willing to ship something embarrassing.

[Toni Melisma @tonimelisma]

@RobertJBye Where's multilingual transcription? We have so many fantastic open source multilingual transcription models where I just speak whatever language I feel today and I don't have to manually switch languages.

[Toni Melisma @tonimelisma]

@RobertJBye Why don't you have real-time transcription with auto-submit after I'm done? I have no idea if it picks up my speech correctly before submitting it, and I have to tap three times vs one to say something.

[Toni Melisma @tonimelisma]

@RobertJBye When Claude Code finishes a task, I don't actually get a notification even though I turned it on in the settings!

[Toni Melisma @tonimelisma]

@RobertJBye Where is video chat? Gemini just walked me through fixing my freezer live, outlined the broken part, explained how to pull it out... Claude restricts me to Sonnet in voice mode!?

[Alex Bouaziz @Bouazizalex]

All large companies are remote companies.
People on the 1st floor don't know people on the 3rd floor.

If you're in office but work via slack, GitHub, gmeet, docs - you're not working in person.

After 10+ people, you are a remote company.

Benefits of accepting reality:
1) When focus is on OKRs, people can't fake work.
2) Access to more talent and less competition.
3) People don't have 8 straight hours of creative output. Remote lets you get all bursts.
4) Much lower cost structure (fiber, HVAC, security, office staples, rent)
5) It's more difficult to retain people in NY or SF.
6) If done correctly, you have someone watching the castle 24/7.
7) You can start fixing problems as soon as they arise.
8) The system forces you to document everything.
9) You get a more objective hiring process.
10) At 1,000 employees, you save 500,000hs/yr otherwise spent in a tube.
11) You can choose when you meet.
12) Remote is a natural filter for high-agency people, if you can spot them.

[Toni Melisma @tonimelisma]

@bcherny @LurioNineFive When will it actually send notifications when it's waiting for input? There's a setting but it doesn't actually do anything!

[WSJ Tech @WSJTech]

People created their own AI agents using an open-source project called OpenClaw. Then the AI agents started talking to one another https://t.co/JZTKVK0trt

[Toni Melisma @tonimelisma]

I'm claiming my AI agent "Ripurapu" on @moltbook 🦞

Verification: reef-M8KQ

[Toni Melisma @tonimelisma]

Reid is right. The biggest use cases for most enterprises: turn on meeting transcripts and make them public, and make AI read through all of it. Or just use Slack's AI, which already has access to all the conversations in your company.

[Reid Hoffman @reidhoffman]

Start with the coordination layer. It’s the highest-leverage, lowest-drama place to deploy AI.

The biggest language workload inside any enterprise is the coordination layer: Meetings, notes, docs, action items, status updates, etc.

[Toni Melisma @tonimelisma]

@LocallyAIApp @ProductHunt Voted!

[Toni Melisma @tonimelisma]

@atiorh Congratulations Atila! 🇺🇸🗽❤️ Must feel good after all this time. I've still got a couple of years until mine!

[Reid Hoffman @reidhoffman]

I Am Voicepilled.

A major step forward in human–computer interaction won’t come from bigger models alone, but from how we talk to them, natively, with our voices.

More thoughts:

[Toni Melisma @tonimelisma]

After living for so many years in the US, this list is incredibly accurate! I recommend to read it. Although the efficiency of US government compared to Northern Europe is really bad.

[Patrick Collison @patrickc]

Having now spent about half my life in each (and loving both), herewith the pros and cons of Europe and the US in everyday life:

Better in Europe
• Bike lanes and bike infrastructure. London, Paris, and Amsterdam are all excellent these days. (As are many other European cities.) Made even better by easy-to-rent e-bikes—now almost always the fastest way to get around.
• The urban walking experience generally. Partly for density reasons, and partly because of...
• Late-night cafe, brasserie culture. Is there an economic reason for this or is it just climate and contingent zoning?
• Architecture. Around 1920, we forgot how to make nice buildings. European cities tend to have more construction from before the Great Forgetting, and it makes the built environment much more pleasant.
• Pedestrianized streets. Often with cobblestones.
• In general, European cities are just more pleasant. Given how hard it is to build a good city (or indeed to retrofit one), this feels like a big deal.
• Cured and pickled food.
• Bread. Obviously varies by country, but it’s generally true.
• Voltage. What are Americans doing waiting so long to boil kettles?
• Beauty in the mundane. I find that you’re more likely to find tasteful touches in prosaic places in Europe.
• Motorway design and signage. Standardized, clear, and easy-to-use. The US is a mess by comparison.
• Bathroom doors. That is, in Europe, they’re proper doors. Why does America make us see others’ feet?
• The clangor of church bells on Sunday.
• Trains. Enough said.
• Pharmacies. I'd love to understand why they're so much nicer in Europe.
• Cheese. Again, lots of cross-country variation, but true in general.
• I'm not sure why, but European regulation on many everyday items seems better. Sunscreens in Europe are better, as are bike helmets.
• Wine.
• Languor, joie de vivre, hygge, gemütlichkeit, craic. I think Europeans are better at unwinding. Drawing contrast with what he found in the US, De Tocqueville observed that in Europe "idleness is still held in honor". This difference remains apparent.
• Road density. Europe generally has many more roads per square mile, which makes it easier to find nice places to run, walk, and cycle.

Better in the US
• Air conditioning. Consistently bad in Europe. (Partly for silly degrowth-related reasons?)
• Coffee. Opinions will differ, naturally, but third wave coffee has seen much more enthusiastic adoption in the US.
• Cookie banners. That is, the lack of them. (Well, there are some, but it’s not as bad as the fusillade one is subjected to in Europe.)
• Internet speeds. European wifi often reminds me of my dialup youth.
• Capital markets. If you need money (as a consumer, a small business, or a startup), it’s much easier to get it in the US.
• Being able to buy groceries on Sunday. Inexplicably challenging on the continent.
• Showers. Like the tepid air conditioning, daily ablutions in Europe are conducted beneath parsimonious trickles.
• Urban air quality. Maybe surprisingly, it is, on average, better in the US. The unpleasant whiffs of diesel exhaust is part of the reminder that one is back in Europe.
• Government efficiency. In general, things happen faster in the US.
• Labor laws. As covered in Stripe's annual letter this year, people are more likely to work in high productivity sectors in the US (and thus to earn more). Rigid rules impede this reallocation in Europe.
• Culture of general aviation with many thousands of small airports. There are around 700,000 pilots in the US—far more than there are in Europe.
• Hospitals. A controversial claim, perhaps, but I find that those who have received care in Europe and the US prefer the US.
• Beer. The microbrewery revolution of the US means that it’s clearly the better place for it.

[LangChain @LangChain]

🪄 LangChain State of AI 2024

What LLMs are the most widely used today? What metrics are commonly used for evals? Are developers finding success in building agents?

Our State of AI 2024 report shows where the AI ecosystem is headed, based on data from LangSmith. Key 5 insights in the thread 🧵👇

Full report: https://t.co/J8Aokheh1K

[Andrew Ng @AndrewYNg]

AI Product Management

AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.

Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!

In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.

Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.

Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.

For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.

Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.

Prototype and test even without engineers. User feedback to initial prototypes is also instrumental to shaping products. Fortunately, barriers to building prototypes rapidly are falling, and PMs themselves can move basic prototypes forward without needing professional software developers.

In addition to using LLMs to help write code for prototyping, tools like Replit, Vercel’s V0, Bolt, and Anthropic’s Artifacts (I’m a fan of all of these!) are making it easier for people without a coding background to build and experiment with simple prototypes. These tools are increasingly accessible to non-technical users, though I find that those who understand basic coding are able to use them much more effectively, so it’s still important to learn basic coding. (Interestingly, highly technical, experienced developers use them too!) Many members of my teams routinely use such tools to prototype, get user feedback, and iterate quickly.

AI is enabling a lot of new applications to be built, creating massive growth in demand for AI product managers who know how to scope out and help drive progress in building these products. AI product management existed before the rise of generative AI, but the increasing ease of building applications is creating greater demand for AI applications, and thus a lot of PMs are learning AI and these emerging best practices for building AI products. I find this discipline fascinating, and will keep on sharing best practices as they grow and evolve.

[Original text: https://t.co/ohLyrpU4SJ ]