I compared the writing style of Opus 5 vs Fable 5, and Opus 5 continues many of the "Claude-isms" of its 4.8 predecessor in a way that Fable broke away from.
Opus 5 still uses "carry the argument", "worth stating plainly", ", and the trap", "The X matters more", the use of "move"
Kinelo | kinelo.com | San Francisco | Onsite (with a bit of flexibility)
Kinelo solves "context myopia": autonomous and semi-autonomous AI agents don't know what they don't know, so they can't search or find the background information required to accomplish tasks accurately and effectively. The result looks like slop but it's not due to model performance, more due to workflow integration. From there, we're rapidly moving into organizing humans together with autonomous "AI coworkers" into workflows that span both. Our goal is to change how organizations are run and structured.
We're hiring for two roles right now:
- Product Engineer / Senior Software Engineer (https://job-boards.greenhouse.io/kinelo/jobs/4205846009) -- do you have the software engineering wisdom not yet encoded in an LLM? and do you have product sense and the willingness and desire to think several steps ahead in the market?
- Founding GTM Lead (https://job-boards.greenhouse.io/kinelo/jobs/4215784009) -- there's no playbook for go-to-market in the category we're creating + the landscape is changing daily. Come architect a strategy and then move mountains to execute on it and help us grow insanely fast.
Competitive salary, meaningful (~first batch of hires) equity, founder previously exited to Apple, strong fundraising history (~$10m).
Imagine if you couldn't buy a lathe unless it refused to make a baseball bat (which could be used for hitting people).
Or if you couldn't buy scissors (because they could cut brake lines).
Or if you couldn't buy a car (because it could be used to run someone over).
And if all of those checked with the government before functioning.
It's almost like maybe instead you should just ban the undesirable end action, enforce that law, and create societal conditions that don't nudge or force people into doing undesirable things.
While commuting can be chaotic, I don't think what you're calling out is the central point of the article.
Spaces that are less chaotic - coffee shops, gyms, even elevators - are places that people used to be able to strike up random short conversations with each other. Sometimes these would become longer. Sometimes they even turned into friendships.
It still happens of course, but I, like the author, am saddened that casual socialization seems to be on the decline.
I like to say Hi to people in elevators. Some reciprocate. Some find it awkward. But I don't even bother saying Hi to those wearing AirPods since that would be rude, interrupting them. Could an interaction have brightened our mutual days? Could we have become friends? Who knows, AirPods preempted it from the start.
We've found that methods like this substantially increase the quality and reliability of coding agent output. The ability to run code in a sandbox, drive an interactive session using a browser or API calls or other apps, and visually confirm output via vision models all adds up to plugging a big hole in the feedback loop for agent modifying a complex codebase.
We've had agents go as far as interactively testing how our product responds in video calls by launching our full stack in a set of docker containers (app, api, db, queues, etc.), all inside a larger sandbox, populating test data, connecting the mock system to a real video call solution like Google meet, and injecting audio and video to test the response. End-to-end, like a real user flow.
It's not perfect yet, but if you are a skeptic on the ability for AI agents to productively modify a complex product, I'd highly encourage you to play with a setup like this before ossifying your conclusions.
To be fair though, models might be changing the calculus for what constitutes a vulnerability that is too small / too obscure to care about.
If AI is reducing the cost of using the long tail of small vulnerabilities or is making possible chaining them together into something more profound, then those small, less-concerning issues might requiring addressing in a way that was previously not required.
Kinelo | kinelo.com | San Francisco | Onsite (with a bit of flexibility)
Kinelo solves "context myopia": autonomous and semi-autonomous AI agents don't know what they don't know, so they can't search or find the background information required to accomplish tasks accurately and effectively. The result looks like slop but it's not due to model performance, more due to workflow integration. From there, we're rapidly moving into organizing humans together with autonomous "AI coworkers" into workflows that span both. Our goal is to change how organizations are run and structured.
We're hiring for two roles right now:
- Product Engineer / Senior Software Engineer (https://job-boards.greenhouse.io/kinelo/jobs/4205846009) -- do you have the software engineering wisdom not yet encoded in an LLM? and do you have product sense and the willingness and desire to think several steps ahead in the market?
- Founding GTM Lead (https://job-boards.greenhouse.io/kinelo/jobs/4215784009) -- there's no playbook for go-to-market in the category we're creating + the landscape is changing daily. Come architect a strategy and then move mountains to execute on it and help us grow insanely fast.
Competitive salary, meaningful (~first batch of hires) equity, founder previously exited to Apple, strong fundraising history (~$10m).
If you want to stand out:
- For the product engineer role, tell us how you think the job of a software engineer is changing.
- For the GTM role, tell us about a time you got massive results with limited resources, all through your ingenuity, creativity, and hard work.
If you would like to learn more about these types of incidents, the US Chemical Safety Review Board has a fantastic series of videos on YouTube: https://www.youtube.com/user/USCSB
They explore the root causes of historical accidents. Importantly, they do it from a broad perspective: not just the chemistry, but the human factors, the decision making, and the process failures that led to the accident and how to prevent such things in the future.
Kinelo | kinelo.com | San Francisco | Onsite (with a bit of flexibility)
Kinelo solves "context myopia": autonomous and semi-autonomous AI agents don't know what they don't know, so they can't search or find the background information required to accomplish tasks accurately and effectively. The result looks like slop but it's not due to model performance, more due to workflow integration. From there, we're rapidly moving into organizing humans together with autonomous "AI coworkers" into workflows that span both. Our goal is to change how organizations are run and structured.
We're hiring for two roles right now:
- Product Engineer (https://job-boards.greenhouse.io/kinelo/jobs/4205846009) -- do you have the software engineering wisdom not yet encoded in an LLM? and do you have product sense and the willingness and desire to think several steps ahead in the market?
- Founding GTM Lead (https://job-boards.greenhouse.io/kinelo/jobs/4215784009) -- there's no playbook for go-to-market in the category we're creating + the landscape is changing daily. Come architect a strategy and then move mountains to execute on it and help us grow insanely fast.
Competitive salary, meaningful (~first batch of hires) equity, founder previously exited to Apple, strong fundraising history (~$10m).
Do you think it's unwise for companies to lock in because they would be better served and get better results by picking and choosing models? Or because by running your business on a single closed provider like Anthropic, you're giving them telemetry they can use to optimize their models and systems to then compete with you later?
Kinelo | kinelo.com | Product Engineering (Backend+AI focus) | San Francisco | Onsite (with flexibility)
We are hiring two Product Engineers to join us in our newly opened office in SF. We're small and early and you'll help shape team culture.
Kinelo solves "context myopia": autonomous and semi-autonomous AI agents don't know what they don't know, so they can't search or find the background information required to accomplish tasks accurately and effectively. The result looks like slop but it's not due to model performance, more due to workflow integration. From there, we're rapidly moving into organizing humans together with autonomous "AI coworkers" into workflows that span both. Our goal is to change how organizations are run and structured.
Our #1 hiring criteria for this position is immense software wisdom, the kind not yet encoded in the weights of an LLM. We are not looking for artisans to meticulously craft single lines of code. We're looking for software engineers excited to use the leverage that AI offers for code production to permit focus on higher order technical architecture, product requirements, and user needs.
We're looking for enough experience that indicates accumulated wisdom in shipping and maintaining reliable, scalable software, solid CS fundamentals, experience taking products from 0 to 1, and versatility.
Our current tech stack includes TypeScript, a managed container service, Postgres, and Redis. But we are adaptive and you should be too. We highly value software engineering and computer science fundamentals and experience with other languages like Go, C++, Python is valued.
Competitive salary, meaningful (~first batch of hires) equity, founder previously exited to Apple, strong fundraising history (~$10m).
It is a precarious time to look backwards on the definition of the roles of an IC or an engineering manager and make any extrapolations to what those will look like in the future.
In my own team, I have seen ICs increasingly function like engineering managers, and even suffer some of the pitfalls of the role switch, as they change from reasoning about creating code to delegating to teams of software agents.
Increasingly, ICs are needing to understand the product roadmap more deeply, figure out how to spec a problem and constraints on a solution in the right way to get their subordinates to produce reasonable output, and be the communication bridge between other jobs functions and the entities actually producing the code.
I've also heard concerns of skill atrophy, as these team members spend less brain energy on language syntax, low level logic, etc, and more on interpreting abstract strategies to solving a problem and pattern matching those strategies against their software engineering wisdom.
If anything, ICs should consider that the skills that will make them successful managing agents might be the ones that have made first-level engineering managers successful: the ability to coordinate with other job functions, map implementation strategies to product and organization needs, and deliberately and carefully delegate and coordinate work of others writing the actual code.
Just keep in mind that there are many highly motivated people directly working on this problem.
It's hard to predict how quickly it will be solved and by whom first, but this appears to be a software engineering problem solvable through effort and resources and time, not a fundamental physical law that must be circumvented like a physical sciences problem. Betting it won't be solved enough to have an impact on the work of today relatively quickly is betting against substantial resources and investment.
Kinelo | kinelo.com | Full-Stack Engineer (AI + Backend) | San Francisco | Onsite
Kinelo manages the new type of hybrid team that will emerge in the future: humans+AI working shoulder-to-“shoulder”.
Our first product helps human software engineering teams deliver more efficiently and more reliably by assisting in coordination and engineering management. But it’s built on a foundational layer that ingests data, knowledge, and process across communications and systems, exposing that to humans and AI alike.
Eventually, Kinelo will become a proper human+AI orchestration layer, managing entire companies, and our goal is to have Kinelo run Kinelo (we already dogfood it today).
We’re looking for high ownership individuals to join our small team and shape its direction. We also just opened a new SF office and new hires will have the ability to help shape company culture, engineering practices, architecture, and roadmap.
We’re well-funded (just raised another round), founded by a serial founder, anti-bureaucratic, and highly technical.
We’re looking for:
- Those who want to go 0 to 1 with high ownership: moving features and products from idea on a napkin, to prototype, to engineering UI, to shipped and polished product
- ~8 years professional experience, including the pre-LLM days with strong CS fundamentals
- A balance of wisdom from experience and optimism about the future
- Strong TypeScript and Postgres (or other RDBMS) experience at scale and with high reliability (SaaS apps, APIs, etc)
- Experience with a variety of technologies, including containerization, distributed systems, and at least one strongly typed programming language (Go, C++, Java, etc)
- Ideally experience with ML and AI systems (though expertise in ML, data science, etc is not needed for this role)
We can’t sponsor visas but relocation assistance to SF is possible.
Kinelo | kinelo.com | Full-Stack Engineer (AI + Backend) | San Francisco | Onsite
Kinelo manages the new type of hybrid team that will emerge in the future: humans+AI working shoulder-to-“shoulder”.
Our first product helps human software engineering teams deliver more efficiently and more reliably by assisting in coordination and engineering management. But it’s built on a foundational layer that ingests data, knowledge, and process across communications and systems, exposing that to humans and AI alike.
Eventually, Kinelo will become a proper human+AI orchestration layer, managing entire companies, and our goal is to have Kinelo run Kinelo (we already dogfood it today).
We’re looking for high ownership individuals to join our small team and shape its direction. We also just opened a new SF office and new hires will have the ability to help shape company culture, engineering practices, architecture, and roadmap.
We’re well-funded (just raised another round), founded by a serial founder, anti-bureaucratic, and highly technical.
We’re looking for:
- Those who want to go 0 to 1 with high ownership: moving features and products from idea on a napkin, to prototype, to engineering UI, to shipped and polished product
- ~8 years professional experience, including the pre-LLM days with strong CS fundamentals
- A balance of wisdom from experience and optimism about the future
- Strong TypeScript and Postgres (or other RDBMS) experience at scale and with high reliability (SaaS apps, APIs, etc)
- Experience with a variety of technologies, including containerization, distributed systems, and at least one strongly typed programming language (Go, C++, Java, etc)
- Ideally experience with ML and AI systems (though expertise in ML, data science, etc is not needed for this role)
We can’t sponsor visas but relocation assistance to SF is possible.
Avy | Staff Engineer / Startup Polyglot Engineer | San Francisco | Onsite (Hybrid) | Full-Time | https://www.avy.app
We are an early-stage, well-funded startup making humans and computers work together more efficiently. Experienced team from Apple AIML and other great companies.
We're looking for a polyglot "startup engineer" at the Staff+ level can help with the end-to-end implementation of new features and capabilities and who can move between different components, be creative, be flexible, take ownership, and get things done. We have a complex, challenging product, but one that's amazing to work on and that will soon change how people think about working with each other and with AI.
You'll enjoy this role if:
- You like going 0 to 1 with high ownership: moving features and products from idea on a napkin, to prototype, to engineering UI, to shipped and polished product
- You are innovative, get satisfaction out of learning new things, and thrive in an environment of uncertainty and opportunity
To succeed, you should:
- Have the wisdom to know when to take the fast route vs when to step back and clean up your mess
- Be capable and excel on your own, but be humble and communicate well with others as projects require more collaboration
- Be versed across many programming languages (e.g. from C++/Java/Swift to Go to JavaScript or Python)
- Have worked on many types of projects and software -- have you built servers, written games, shipped native apps, and vibe-coded (kidding) web apps? If so, you're a fit.
- Be versatile and work both high level and low level, on old tech and new. Have you both prompt-engineered an LLM AND tweaked the code that decodes tokens in your own local LLM runner? If so, you're a fit.
Avy | Staff Engineer / Startup Polyglot Engineer | San Francisco or Salt Lake City | Onsite (Hybrid) | Full-Time | https://www.avy.ai
We are an early-stage, well-funded startup making humans and computers work together more efficiently. Experienced team from Apple AIML and other great companies.
We will be opening several roles soon, but are previewing this one early: Staff Engineer / Startup Polyglot Engineer.
You'll enjoy this role if:
- You like going 0 to 1 with high ownership: moving features and products from idea on a napkin, to prototype, to engineering UI, to shipped and polished product
- You are innovative, get satisfaction out of learning new things, and thrive in an environment of uncertainty and opportunity
To succeed, you should:
- Have the wisdom to know when to take the fast route vs when to step back and clean up your mess
- Be capable and excel on your own, but be humble and communicate well with others as projects require more collaboration
- Be versed across many programming languages (e.g. from C++/Java/Swift to Go to JavaScript or Python)
- Have worked on many types of projects and software -- have you built servers, written games, shipped native apps, and vibe-coded (kidding) web apps? If so, you're a fit.
- Be versatile and work both high level and low level, on old tech and new. Have you both prompt-engineered an LLM AND tweaked the code that decodes tokens in your own local LLM runner? If so, you're a fit.
All said, we're looking for the stereotypical "startup engineer" who can move between different components, be creative, be flexible, take ownership, and get things done. We have a complex, challenging product, but one that's amazing to work on and that will soon change how people think about working with each other and with AI.
1. I stumbled for a while looking for the license on your website before finding the Apache 2.0 mark on the Hugging Face model. That's big! Advertising that on your website and the Github repo would be nice. Though what's the business model?
2. Given the LLama 3 backbone, what's the lift to make this runnable in other languages and inference frameworks? (Specifically asking about MLX but Llama.cpp, Ollama, etc)
Using a grammar to force decoding say valid JSON would work, but that hasn't always been available in the implementations we've been using (like MLX). Solvable by software engineering and adding that to the decoders in those frameworks, but fine tuning has been effective without that work.
The bigger thing though was getting the models to have the appropriate levels of verbosity and detail in their ouput which fine tuning made more consistent.
If you think in long timeframes, want to build for the future of software, join us at kinelo.com
Generally happy to chat (except recruiters...): keith [at] kinelo.com