Finbarr Taylor

On a simple level, Parlant seeks to solve one problem: getting conversational LLM agents under control. And that can make it sound like a feature - like the missing piece for many existing frameworks (either blackbox or open-source). Why it actually isn’t a feature becomes clearer once we start understanding the problem on a deeper level. 2 years ago, disruptive AI vendors promised the omniscient LLM that can do everything and replace anything. Now, some parts of the market, particularly certain vendors, still haven’t given up on selling this illusion, even though most buyers and users have already awakened to it. I’ve come to the conclusion that every time we (humans) face a new technological breakthrough, we’re quick to assume that humanity has been decoded. For example, when mass construction got popular, we understood ourselves as such: “He’s got a loose screw.” When steam engines came out, “He ran out of steam”. With electronics, “He burnt a fuse,” and so on. Just like we eventually understood that those things are poor “imitiations” and are far from capturing the complexity of human faculties, more people are starting to understand that LLMs - in some vague, odd way - *only awkwardly capture some parts* of what it means to be and think like a human. But most LLM frameworks are still stuck on the “omnisicient LLM” illusion; that it will fill in the gaps and figure out how to connect the dots. Thus, the premise of today's many generic “LLM app frameworks” follows this reasoning: 1. You got this “thing" that can do everything. 2. There are certain best practices on how to configure it - “we’ll do that for you." 3. It needs to connect to your resources - “we’ll provide quick integrations for you." 4. Done. The "thing" does the rest. This is essentially why they are called “wrappers”. In reality, the “thing" _so_ doesn’t do “the rest.” Circling back, Parlant provides control and power over one thing in particular: Conversation Dynamics. It doesn’t get your LLM to know the answer to complex questions. It doesn’t make it easier for you to parse your knowledge-bases and keep them up to date. Instead, it solves a (different) clear business problem: It allows you to make your agent a controlled, consistent, and effective communicator for your organization - according to your rules, however many you have. https://lnkd.in/dD3GAKYe

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Debugging in climate tech isn't just about squashing bugs it's about securing trust. Each fix at Plantd.Life improved not just the code, but the experience for the schools, fundraisers, and changemakers using it. Mistakes don't derail missions. They strengthen them."

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Building a startup is hard. It is especially hard in a country like Nigeria, where the ecosystem is yet to mature, and there are still many challenges to navigate. It is confusing, lonely, and full of uncertain decisions. Founders, especially early-stage founders, need as much support as they can get. Not “inspire to perspire” posts, but practical support on hiring, operations, funding, customer research, and navigating the regulatory landscape. Too many founders are surrounded by noise, surface-level advice, and generic content that does not reflect their current realities. What they need is a community that understands exactly what they are going through and can offer context that is immediately useful. Sierra Collective exists to be that community. A space where founders learn from one another’s live experiences and shorten the painful trial and error that usually defines the early stages of building. At Sierra, we believe that the most valuable support comes from those who are active in the trenches, navigating the same obstacles. If you are an early-stage founder, you should join the Sierra Collective. You do not have to figure everything out alone. As the African proverb says, “If you want to go fast, go alone. If you want to go far, go together.” Join Sierra: https://lnkd.in/d-Nzmtkq

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Decision modeling is key. Intent + decision traces created by agents need to be modeled to create self learning or ever improving decision quality ( measured via outcomes), while the criteria is non stop shifting. Process modeling - aka crud on system of records will also get eroded and merged with decisions models being stored. This is where Agebt plane and human plane converge.

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I was at GDC in San Francisco last week, and it reinforced something I’ve seen at conferences for years: Most people think the value is in the event itself. It’s not. The real connections—the ones that lead to ideas, opportunities, and partnerships—happen outside the main stage. Over dinner. In smaller conversations. In the moments between sessions. And often, they happen because someone did a little homework before the event even started. The people you’d most like to meet are often in the same place as you for a short window. That’s a rare opportunity. But if you just show up and hope for the best, you’ll likely miss it. A simple shift: Don’t just attend—prepare. Identify who you’d like to meet. Reach out in advance if you can. Be intentional with your time while you’re there. Conferences can be incredibly valuable—but only if you treat them that way. What’s one thing you do to make the most of events like this?

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Today we’re sharing some big news. Ando raised a $4M seed round, led by Slow Ventures, with participation from Blitzscaling Ventures, Zero Capital, Monochrome Capital, Gaingels, Mana Ventures, Fireroad, and additional investors. Hourly work powers a huge part of the economy but scheduling is still a mess. Too often, staffing doesn’t match real demand, shifts change at the last minute, and workers are left trying to juggle childcare, commutes, school pickup, and second (or third) jobs. That chaos is expensive for businesses and brutal for workers. Ando exists to make hourly work more predictable. We use AI to forecast demand for each location and help teams build schedules that fit real people. We’ve been in the field with enterprise customers for over a year, and the early traction is encouraging: - 90%+ daily demand accuracy  - 100% conversion from presentation → pilot → paid platform - 100% retention so far In some locations, customers have seen $60K–$80K in incremental revenue (results vary and aren’t solely attributable to Ando) Most importantly, this is about treating workers’ time like it matters. If you’re working multiple jobs, a last-minute schedule change isn’t a small inconvenience, it can blow up your week. This funding will help us expand our forecasting and scheduling platform and scale deployments with enterprise customers. Grateful to our investors, advisors, customers, and team. And if you’re an operator or leader living this problem every day, I’d love to connect. Full release below. https://lnkd.in/gyYjqmts #Ando #AI #ArtificialIntelligence #WorkforceAI #FutureOfWork #LaborTech #RestaurantTech #RetailTech

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U.S. energy demand is surging — don’t get left behind. New gas projects and large industrial loads are reshaping power markets at breakneck speed. In this high-stakes landscape, timely and accurate data is essential. Halcyon’s Data Products, powered by a regularly updated authoritative data catalog of 4.5+ million documents from all 50 public utility commissions, ensure you always have the latest energy market intelligence. Download this guide to see how AI can help power sector stakeholders find new growth opportunities first.

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