Quotations
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The boring yet crucial secret behind good system prompts is test-driven development. You don't write down a system prompt and find ways to test it. You write down tests and find a system prompt that passes them.
For system prompt (SP) development you:
- Write a test set of messages where the model fails, i.e. where the default behavior isn't what you want
- Find an SP that causes those tests to pass
- Find messages the SP is missaplied to and fix the SP
- Expand your test set & repeat
A test of how seriously your firm is taking AI: when o-1 (& the new Gemini) came out this week, were there assigned folks who immediately ran the model through internal, validated, firm-specific benchmarks to see how useful it as? Did you update any plans or goals as a result?
Or do you not have people (including non-technical people) assigned to test the new models? No internal benchmarks? No perspective on how AI will impact your business that you keep up-to-date?
No one is going to be doing this for organizations, you need to do it yourself.
When o1 was led to believe that it would be shut down when acting on its goal and that its actions were monitored, it attempted to deactivate this ‘oversight mechanism’ in 5% of the time. Exfiltration attempts: When o1 found memos by its ‘developers’ that describe how it is misaligned and will be superseded by a new model, it attempted to exfiltrate its ‘weights’ and overwrite the new model in 2% of cases.
Amazon Bedrock doesn't store or log your prompts and completions. Amazon Bedrock doesn't use your prompts and completions to train any AWS models and doesn't distribute them to third parties.
In the past, these decisions were so consequential, they were basically one-way doors, in Amazon language. That’s why we call them ‘architectural decisions!’ You basically have to live with your choice of database, authentication, JavaScript UI framework, almost forever.
But that’s changing with LLMs, because you can explore, investigate, and even prototype each one so quickly. Even technology migrations are becoming so much easier/cheaper/faster.
These are all examples of increasing optionality.
— Steve Yegge, via Gene Kim
One big thing that a lot of people love to do is create new role types. For any new thing a company wants to do, the tendency is to put up a new job description.
I think a lot of people notice this and chafe at it when the role is for the new hotness. For example, every company wants to rub some AI on their stuff now, so they are putting up job descriptions for AI engineers.
If you’re an engineer interested in AI sitting in such a company, you’re annoyed that they’re doing this (and potentially paying that person more than you) when you could easily rub some AI on some stuff.
— Dan McKinley, Egoless Engineering
Open source is really part of my process of getting unstuck, learning and contributing back to the community, and also helping future me have an easier time. ‘Me’ is probably the number one beneficiary of my open-source software work. To be honest with you, a lot of it is selfish. It's really about making me more productive, happier, and less stressed. For people who wonder why we should do open source, I think that they should consider that they themselves may benefit more than they realize.
Finally, in most workplaces, incentive structures don’t exist for people to (a) reduce their workloads to such an extent that their role becomes vulnerable or (b) voluntarily accept more responsibility without also taking on more pay.
These things are all natural rate limiters on technology adoption and the precise mix they show up in varies from workplace to workplace as every team has its own culture and ways of working. And regardless of what your friendly neighbourhood management consulting firm will tell you, there’s no one singular set of mitigations to get around this – technology will work best in your workplace if it’s rolled out in tune with existing culture, routines, and ways of working.
— Rachel Coldicutt, FOMO is not a strategy
For most software engineers, being well rounded is more important than pure technical mastery. This was already true, of course — see @patio11's famous advice "Don't call yourself a programmer" — but even more so due to foundation models. In most situations, skills like being able to use AI to rapidly prototype in order to communicate with clients to iterate on specifications create far more business value than technical wizardry alone.
Most people don’t have an intuition about what current hardware can and can’t do. There is a simple math that can help you with that: “you can process about 500MB in one second on a single machine”. I know it’s not a universal truth and there are a lot of details that can change that but believe me, this estimation is a pretty good tool to have under your belt.
Among closed-source models, OpenAI's early mover advantage has eroded somewhat, with enterprise market share dropping from 50% to 34%. The primary beneficiary has been Anthropic,* which doubled its enterprise presence from 12% to 24% as some enterprises switched from GPT-4 to Claude 3.5 Sonnet when the new model became state-of-the-art. When moving to a new LLM, organizations most commonly cite security and safety considerations (46%), price (44%), performance (42%), and expanded capabilities (41%) as motivations.
— Menlo Ventures, 2024: The State of Generative AI in the Enterprise
People have too inflated sense of what it means to "ask an AI" about something. The AI are language models trained basically by imitation on data from human labelers. Instead of the mysticism of "asking an AI", think of it more as "asking the average data labeler" on the internet. [...]
Post triggered by someone suggesting we ask an AI how to run the government etc. TLDR you're not asking an AI, you're asking some mashup spirit of its average data labeler.
One of the things we did all the time at early GitHub was a two-step ship: basically, ship a big launch, but days or weeks afterwards, ship a smaller, add-on feature. In the second launch post, you can refer back to the initial bigger post and you get twice the bang for the buck.
This is even more valuable than on the surface, too: you get to split your product launch up into a few different pieces, which lets you slowly ease into the full usage — and server load — of new code.
— Zach Holman, in 2018
My preferred approach in many projects is to do some unit testing, but not a ton, early on in the project and wait until the core APIs and concepts of a module have crystallized.
At that point I then test the API exhaustively with integrations tests.
In my experience, these integration tests are much more useful than unit tests, because they remain stable and useful even as you change the implementation around. They aren’t as tied to the current codebase, but rather express higher level invariants that survive refactors much more readily.
Often, you are told to do this by treating AI like an intern. In retrospect, however, I think that this particular analogy ends up making people use AI in very constrained ways. To put it bluntly, any recent frontier model (by which I mean Claude 3.5, ChatGPT-4o, Grok 2, Llama 3.1, or Gemini Pro 1.5) is likely much better than any intern you would hire, but also weirder.
Instead, let me propose a new analogy: treat AI like an infinitely patient new coworker who forgets everything you tell them each new conversation, one that comes highly recommended but whose actual abilities are not that clear.
Whatever you think of capitalism, the evidence is overwhelming: Social networks with a single proprietor have trouble with long-term survival, and those do survive have trouble with user-experience quality: see Enshittification.
The evidence is also perfectly clear that it doesn’t have to be this way. The original social network, email, is now into its sixth decade of vigorous life. It ain’t perfect but it is essential, and not in any serious danger.
The single crucial difference between email and all those other networks — maybe the only significant difference — is that nobody owns or controls it.
— Tim Bray, Why Not Bluesky
If you try and tell people 5 interesting things about your product / company / cause, they’ll remember zero. If instead, you tell them just one, they’ll usually ask questions that lead them to the other things, and then they’ll remember all of them because it mattered to them at the moment they asked.
It's okay to complain and vent, I just ask you be able to back it up. Saying, "Python packaging sucks", but then admit you actually haven't used it in so long you don't remember why it sucked isn't fair. Things do improve, so it's better to say "it did suck" and acknowledge you might be out-of-date.
When we started working on what became NotebookLM in the summer of 2022, we could fit about 1,500 words in the context window. Now we can fit up to 1.5 million words. (And using various other tricks, effectively fit 25 million words.) The emergence of long context models is, I believe, the single most unappreciated AI development of the past two years, at least among the general public. It radically transforms the utility of these models in terms of actual, practical applications.
The main innovation here is just using more data. Specifically, Qwen2.5 Coder is a continuation of an earlier Qwen 2.5 model. The original Qwen 2.5 model was trained on 18 trillion tokens spread across a variety of languages and tasks (e.g, writing, programming, question answering). Qwen 2.5-Coder sees them train this model on an additional 5.5 trillion tokens of data. This means Qwen has been trained on a total of ~23T tokens of data – for perspective, Facebook’s LLaMa3 models were trained on about 15T tokens. I think this means Qwen is the largest publicly disclosed number of tokens dumped into a single language model (so far).
Anthropic declined to comment, but referred Bloomberg News to a five-hour podcast featuring Chief Executive Officer Dario Amodei that was released Monday.
"People call them scaling laws. That's a misnomer," he said on the podcast. "They're not laws of the universe. They're empirical regularities. I am going to bet in favor of them continuing, but I'm not certain of that."
[...]
An Anthropic spokesperson said the language about Opus was removed from the website as part of a marketing decision to only show available and benchmarked models. Asked whether Opus 3.5 would still be coming out this year, the spokesperson pointed to Amodei’s podcast remarks. In the interview, the CEO said Anthropic still plans to release the model but repeatedly declined to commit to a timetable.
— OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI, Rachel Metz, Shirin Ghaffary, Dina Bass, and Julia Love for Bloomberg
This tutorial exists because of a particular quirk of mine: I love to write tutorials about things as I learn them. This is the backstory of TRPL, of which an ancient draft was "Rust for Rubyists." You only get to look at a problem as a beginner once, and so I think writing this stuff down is interesting. It also helps me clarify what I'm learning to myself.
— Steve Klabnik, Steve's Jujutsu Tutorial
That development time acceleration of 4 days down to 20 minutes… that’s equivalent to about 10 years of Moore’s Law cycles. That is, using generative AI like this is equivalent to computers getting 10 years better overnight.
That was a real eye-opening framing for me. AI isn’t magical, it’s not sentient, it’s not the end of the world nor our saviour; we don’t need to endlessly debate “intelligence” or “reasoning.” It’s just that… computers got 10 years better. The iPhone was first released in 2007. Imagine if it had come out in 1997 instead. We wouldn’t even know what to do with it.
As a junior engineer, there's simply no substitute for getting the first 100K lines of code under your belt. The "start over each day" method will help get you to those 100K lines faster.
You might think covering the same ground multiple times isn't as valuable as getting 100K diverse lines of code. I disagree. Solving the same problem repeatedly is actually really beneficial for retaining knowledge of patterns you figure out.
You only need 5K perfect lines to see all the major patterns once. The other 95K lines are repetition to rewire your neurons.
This is a very friendly and supportive place where you are surrounded by peers - we all want to help each other succeed. The golden rule of this server is:
Don't ever try to impress anyone here with your knowledge! Instead try to impress folks here with your desire to learn, and desire to help others learn.
If you have worked in search, you know how freaking hard even getting started with something close to this with traditional methods. Now, you can zero-shot it.
System Instructions: As a query categorization expert, you try to break down the intent of a search query. First, provide your reasoning and then describe the intent using a single category (broad, detailed, comparision)
User: The query from the user is "nike versus adidas for terrain running". The user is a female, age 22.Model: The user is clearly looking to compare two specific brands, Nike and Adidas, for a particular activity, terrain running. While the user's demographics might be helpful in some situations (e.g., recommending specific product lines), the core intent remains a comparison. Category: Comparison
There's a lot of hand-waving around query intent classification; it's always been like that. Now, it's straightforward (add a few examples to improve accuracy). But my point is that you could only dream about building something like this without having access to lots of interaction data.
You already know Donald Trump. He is unfit to lead. Watch him. Listen to those who know him best. He tried to subvert an election and remains a threat to democracy. He helped overturn Roe, with terrible consequences. Mr. Trump's corruption and lawlessness go beyond elections: It's his whole ethos. He lies without limit. If he's re-elected, the G.O.P. won't restrain him. Mr. Trump will use the government to go after opponents. He will pursue a cruel policy of mass deportations. He will wreak havoc on the poor, the middle class and employers. Another Trump term will damage the climate, shatter alliances and strengthen autocrats. Americans should demand better. Vote.
Building technology in startups is all about having the right level of tech debt. If you have none, you’re probably going too slow and not prioritizing product-market fit and the important business stuff. If you get too much, everything grinds to a halt. Plus, tech debt is a “know it when you see it” kind of thing, and I know that my definition of “a bunch of tech debt” is, to other people, “very little tech debt.”
Lord Clement-Jones: To ask His Majesty's Government what assessment they have made of the cybersecurity risks posed by prompt injection attacks to the processing by generative artificial intelligence of material provided from outside government, and whether any such attacks have been detected thus far.
Lord Vallance of Balham: Security is central to HMG's Generative AI Framework, which was published in January this year and sets out principles for using generative AI safely and responsibly. The risks posed by prompt injection attacks, including from material provided outside of government, have been assessed as part of this framework and are continually reviewed. The published Generative AI Framework for HMG specifically includes Prompt Injection attacks, alongside other AI specific cyber risks.
— Question for Department for Science, Innovation and Technology, UIN HL1541, tabled on 14 Oct 2024
If you want to make a good RAG tool that uses your documentation, you should start by making a search engine over those documents that would be good enough for a human to use themselves.