James Barrese

I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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Thanks for sharing these insights. What I am seeing is that orgs test an idea using a proprietary #LLM like #OpenAI, then put it into production with an open source LLM using a development platform like #Llamaindex. That way, private data remains private.

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Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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This is a great list, and a good example of fractal complexity inherent in seemingly "solved" problems. But: most frameworks handle the majority of these out of the box for you — except for SSO / SAML, which should probably not be a pre-launch concern. More broadly: software is about making decisions and trade-offs with varying degrees of confidence. Sometimes those decisions are very clearly "build vs. buy"-shaped; sometimes they're more esoteric. You owe it to yourself as an engineer and operator to make those kinds of decisions not because you think "well, this is what everyone says you gotta do", but because you've weighed the pros and cons and come to a conclusion, even if you don't know everything about the problem space.

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Insane: What groq can do w/ open source llama3 is impressive speed wise and not only. https://groq.com Given: you are an advanced Porsche mechanic, working on a 986 Boxster. When driven aggressively, on right turns, the rear makes a grinding noise and something seems to slip. All suspension components are new. Left axle was changed but no difference. - on left turn the problem does not happen. Problems started during a day at the track. A challenging problem to diagnose! As an advanced Porsche mechanic, I'll take a systematic approach to identify the root cause of the grinding noise and slipping sensation on right turns. Given that the problem started during a day at the track, it's likely that the issue is related to the car's dynamics and suspension behavior under high-performance driving conditions. Here's my initial hypothesis: 1. **Tire wear and imbalance**: Aggressive driving on the track may have caused uneven tire wear, which could lead to vibrations and noise. I'll inspect the tires for any signs of uneven wear, and check the tire pressure to ensure it's within the recommended specifications. 2. **Suspension bushing wear**: Although the suspension components are new, it's possible that the bushings are worn out or damaged, causing the grinding noise. I'll inspect the bushings and replace them if necessary. 3. **Rear axle bearing wear**: The grinding noise could be caused by worn-out or damaged rear axle bearings. I'll inspect the bearings and replace them if necessary. 4. **Rear wheel bearing wear**: Similar to the axle bearings, worn-out or damaged rear wheel bearings could cause the grinding noise. I'll inspect the bearings and replace them if necessary. 5. **CV joint wear**: The CV joints could be worn out or damaged, causing the grinding noise. I'll inspect the CV joints and replace them if necessary. 6. **Power steering system**: The power steering system could be causing the grinding noise, especially if the system is not functioning properly. I'll inspect the power steering system and replace any faulty components if necessary. 7. **Rear differential**: The rear differential could be causing the grinding noise due to worn-out or damaged components. I'll inspect the differential and replace any faulty components if necessary. To further diagnose the issue, I'll perform the following tests: 1. **Vibration test**: I'll drive the car on a smooth surface and apply gentle to moderate braking while turning right. This will help me identify if the vibration is caused by a specific component or the suspension. 2. **Noise analysis**: I'll use a sound level meter to measure the noise level and frequency to determine if it's related to a specific component or the suspension. 3. **Suspension test**: I'll perform a suspension test to identify if the issue is related to the suspension components or the bushings. Based on the test results, I'll develop a plan to address the issue and get the car running smoothly and quietly again.

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Tembo is leading in the 'Postgres as a Platform' movement. Their most recent blog post(s) below do a pretty good job of explaining why 'Postgres as a Platform' is rapidly becoming an anecdote for database sprawl.

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We are quickly entering the "trough of disillusionment" in the hype cycle for generative AI. While GPTs excel in automating certain tasks (and can even automate away large portions of some jobs) they often fall short in others. Like all new technology, the issue lies in misapplying generative AI for tasks it wasn't designed for. This is a problem compounded by the GPTs themselves suggesting they can handle these tasks (often cheerfully!). This misalignment creates frustration and unmet expectations which we're starting to see more of these days. As the dust settles I firmly believe we'll recognize GPTs as a transformative technology across many industries. However it's crucial to understand their limitations and apply them appropriately. What are your thoughts on the current state of generative AI and its future applications?

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Great take from my good friend Shelly Palmer on why and how more and more companies benefit from implementing a Chief AI Officer role. Executives that can connect the dots between cutting-edge AI and business and product needs are critical to move beyond "GenAI hype" and demos to large scale, value-creating productization of this disruptive technology. #artificialIntelligence #largelanguagemodels #AIrevolution

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The next stage of AI stocks outperforming the market began sometime this month imho. And my favorite stock is ServiceNow. Some AI hardware stocks will lack performance in the second part of the year - not Nvidia, as this is an AI ecosystem play ( i sold July 140 Calls on 50 percent of my NVDA position this week, it is a nice return for 3.5 weeks holding period, if the shares melt up I will do a roll over to e.g September 160) On the other hand, the first AI application winners will show strong price action. ServiceNow is the number-one company mentioned in a recent poll. There is no time to describe the ServiceNow business model in more detail, but I favor them over stocks like Adobe or Salesforce. It is the fourth biggest position in my portfolio. Top 5 Holdings Number one - Nvidia Number two - Eli Lilly Number three - Microsoft Number four - ServiceNow Number five - Nu Holding I am happy to buy the dips. A stock such as SuperMicro desperately needs a new catalyst to continue its meteoric advice. I am a bit more skeptical that this can continue. I am putting the SMCI chart in the comments. Happy Trading!

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At Eightfold we are working on Action Engineering framework to enable LLMs to take actions across a variety of underlying platforms. We can't wait for every underlying system to be Generative AI enabled. We will start publishing a lot more around the framework in coming weeks and months. Moving from prompt engineering to action engineering. #actionengineering #promptengineering

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Eric S. Yuan (he / him / his), Zoom CEO: Digital avatars attending meetings & handling emails are the future 🤖 Personalized AI twins, trained on your data, making decisions on your behalf 💡 I'm bullish on this vision. I already see many startups automating workflows for enterprise personas. Task-specific AI agents will be prominent in 2 years. What's needed short-term? 1️⃣ Better customized enterprise LLMs, learning from continuous human feedback 2️⃣ Nearly no hallucinations for autonomous actions 3️⃣ Robust authentication for non-human identities (a unicorn opportunity in the identity stack) Read the interview - Zoom aims to be a key player in this space. #largelanguagemodels #llms #ai #ml #aiagents #digitaltwin #digitalavatar #enterpriseautomation #startups https://lnkd.in/ggPr-4-R

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Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

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Excited to share that M12, Microsoft's Venture Fund, has invested in Builder.io; a visual headless content management system (CMS) with a focus toward AI-driven design and development! It should come as no surprise that developers are spending less time coding and more time on operational responsibilities. This constraint combined with limited resources creates a perfect storm; one where developers are eager to get their time back so they can focus on developing applications and deploying code.   Builder.io helps address this underlying issue by giving developers the ability to pick and choose how they want to customize applications while simultaneously granting specific rights to non-technical stakeholders so they can make meaningful contributions via visual editor to various properties such as landing pages, marketing websites, ecommerce storefronts, mobile apps, and internal applications. However, a solution is only as good as the team behind it. We're equally excited to partner with and back founders such as Steve Sewell and Brent Locks; two individuals whose passion is matched by their desire to push the boundaries for enterprise-scale digital experiences. #M12 #MicrosoftVentureFund #Builder.io

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"Overall, I’d characterize the ecosystem as iterating. OpenAI and GitHub launched their features at roughly $20-30 per month. This initial pricing has anchored the market at least for now in that range. Microsoft & ServiceNow have stated AI features increased productivity by approximately 50 percent. If buyers act rationally & reduce headcount by 50%1 which we know is probably not true, then to maintain the same revenue per customer, price would need to double. We can observe that in three of the companies’ pricing strategy above. If pricing really does provide information (see the work of Mauboussin), then these companies are pricing in a 40% productivity gain. This is for copilots." https://lnkd.in/gRcM9JDc

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OpenAI today announced their new GPT-4o model. Here are the key points you should know: Omni: The "o" in GPT-4o stands for Omni. OpenAI is now focusing on a cross-functional user experience, integrating text, audio, and images into the same AI pipeline. Unlike previous versions of ChatGPT, where you had to convert images into prompts or text into voice, the Omni model offers a seamless experience. This results in faster and more intuitive interactions. Improved Performance: GPT-4o is faster, a LOT FASTER, and more capable across many tasks. It outperforms previous versions of GPT-4, as well as popular models like Gemini and Claude, in numerous publicly listed tasks and test comparisons. While further research will validate these claims, the progress across text, audio, and other AI applications is impressive. When you don't have to wait, you can iterate on prompts and answers much more quickly. Desktop Version: OpenAI is releasing a desktop version of GPT-4o that can access the context of your desktop and screen. This feature enables various collaborative scenarios where ChatGPT understands the application in use. This is perhaps what Clippy was always trying to be, only this AI is exceptionally more helpful. Another significant development is that AI computation is now happening both on-device and off-device. This is expected to be further enhanced by upcoming technology from Apple, likely through next-generation assistants. Additionally, streaming camera and audio input allows for fluid conversations, interruptions, and continuations without losing context. Despite the technology being only a day old, my initial experience over the past few hours indicates that while the AI revolution is well underway, the UX revolution driven by these technologies is just beginning.

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Can’t agree more on this as we are seeing more early stage startups building more useful AI-native agents instead of copilots on existing workflow.

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Winter is coming... or maybe not. One or two bubbles might burst, such as generative AI startups lacking real problem-solving solutions. The articles below are worth reading, but in any case, buckle up – 2024 will be a roller coaster! Perhaps it's time to get back to fundamentals: solving problems, not just riding the wave. #Innovation #AI #Startups #TechTrends #2024Predictions #BusinessStrategy #ProblemSolving Hedge funds are dumping tech stocks despite their recent rally, says Goldman Sachs "Meanwhile, hedge funds’ exposure to “momentum stocks” is on pace to decline for the first time in six months, the Goldman analysts said — adding that the funds’ exposure to “long concentration” and “long crowdedness” have both seen notable declines and are at their lowest levels so far in 2024. That suggests that “long-short fund managers have become more mindful of a potential drawdown in those factors after strong returns year to date,” Lin and his team said." https://lnkd.in/g2Djzz7d Is an AI stock bubble looming? That’s the $167 billion question. Will the frenzied spending on artificial intelligence drive major revenue boosts for internet companies — or are those big spenders getting ahead of their skis? That’s a key question for investors these days, according to Barclays analyst Ross Sandler, who noted Tuesday that the math doesn’t appear to add up. Wall Street expectations are for roughly $60 billion in incremental capital expenditures on AI but just $20 billion in incremental cloud revenue for 2026. https://lnkd.in/gZATA5b5

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