Grant Singleton

Dev agencies, if you don’t want to be treated as a commodity, stop acting like one. - Learn the business context for engineering needs - Stop squeezing your clients every opportunity you get - Prioritize transparency, communication, and accountability In short, position yourself as a partner that uses tech to solve business problems. You really can’t complain about being seen as a commodity if you treat everything as a transaction.

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Chasing quick quotes from dev agencies? You’re setting yourself up for failure. Lowball offers lead to: - Radio silence when you need updates - Misunderstanding your needs - Bait-and-switch developers - Spaghetti code - Endless delays Price matters. Credibility, communication, and customer service matter more.

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You can tell early on if a dev agency will act in your best interests. Some agencies aim to hook you and create dependency by: - Withholding admin access to GitHub repos - Limiting direct communication with the dev team - Failing to document work for future developers These are red flags that will harm your business. When we begin a project, we focus on building a strong relationship from the start. How? Instead of chasing a quick profit or locking the client into an endless engagement, we’ll start with a narrower scope that still delivers value. If a dev agency acts against its own short-term interests, it’s a positive sign. I’d rather leave money on the table in the interest of building trust.

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Software engineers tend to be attached to their favorite tools. If you’re working with a dev agency, consider this: They’ll make tech choices that you'll live with long-term. When you eventually build your in-house team, you’ll inherit the impact of their decisions. - Is there a supportive tech community? - Is there up-to-date documentation on APIs and libraries? - Will you be able to hire developers who know or can quickly learn the stack? There are lots of factors in making tech choices. Even if you’re not technical, understand why the agency makes its decisions.

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Have a lot of github repos in your org but aren't sure how to see them all in a clear, concise way? Use this command: gh repo list <org_name_here> --limit 1000

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Here's a quick look at a new PDF + Screenshot gem I've been building. It uses Ferrum to talk to headless Chrome without any other dependencies and integrates nicely into Rails. https://lnkd.in/g65Uwx3k

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I've seen lots of posts about the dangers of tech debt, but it’s not a question of whether your codebase has it or not. Every codebase does. It’s just a question of degree. Code can always be improved. It can always be optimized for performance, readability, scalability, security, etc. The big question is the short-term, mid-term, and long-term needs of the business.

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One reason AI isn't creating as much value as expected is: On a micro level, each individual is becoming more efficient. For instance, with GitHub Copilot, a software engineer can produce work at double the speed. While individual productivity is up, the bottleneck lies within the organization. We still need to collaborate, resolve tickets, hold meetings, and set goals. Even if each step is faster, there are overarching bottlenecks in the underlying system. Imagine running an art studio. AI allows us to draw much faster, but we still spend the same effort acquiring users. The demand remains the same, say 10 units of artwork. Even if production is faster and requires fewer people, the overall outcome isn't significantly different. In a more complex organization, this effect is amplified. Other processes delay the entire pipeline, preventing the full value of automation from being realized. Take AI phone call agents as an example. If we can't completely eliminate the need for a person to take the call, having AI assistance doesn't make much economic sense. The sectors seeing the most demand increase are in GPUs, foundation models, and perhaps entertainment. Simply increasing productivity does not result in the same scale of outcome at the moment. However, as we streamline the pipeline and resolve these bottlenecks, the potential for AI to deliver significant value can be fully unleashed.

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G Force Data just solved a problem for the Texas DOT saving $1 mil/year in lost employee time. The problem was application latency that was costing a combined 1000 hours/month of lost time for a team of ~200 users. We were hired to review the database layer for potential performance issues and recommend/implement improvements. No obvious database offender was discovered. Latency issues continued after several database (and application) best practice improvements were implemented to rule them out. We couldn’t resist investigating beyond the database as we had suspicions we couldn’t ignore. After arduous monitoring and investigating…bingo! We discovered an obscure, OS-level, disk-related process executing on a schedule matching the timing pattern of the latency reports. After resolving the issue, the latency was gone! We’re so happy to work with (and have the trust of) our government customers and look forward to continuing our relationships and support of their goals.

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Any tips on getting a @nextjs + @turborepo setup to run a bit faster? Cold boot takes ~20 seconds. HMR is about the same.

Great post from Will Ahmed at WHOOP. We are in the fortunate position at Memfault to work with many innovative hardware companies - at all stages in their development lifecycle (including Whoop). We see first-hand how hard launching these products can be. It’s the reason Memfault exists. If I were to add a couple of additional recommendations to Will’s list they would be: 1. Agree on a limited number of very clear signals for product quality that you can measure consistently, that everyone on your team, top-to-bottom, understands, and that are closely linked to the core value proposition of your product. These measures should act as the early warning system if something isn’t as it should be - they should be the benchmark for go / no-go decisions. "Is device performance acceptable across these key measures?" Yes/No. And if the answer is yes - that should be enough to ship with confidence, even if you know other things aren’t perfect. 2. Users are extremely unpredictable and there is no substitute for real-world data. With all the skill in the world (and even a heavy dose of good luck) there is ZERO chance you can predict upfront exactly how your users will actually use your product. Of course, we are biased here. But seriously - how you think your product will be used, and how it’s actually used, can be dramatically different. And this in turn can have a significant impact on what is deemed acceptable for reliability and performance.

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Plaid did $300m + in revenue and has a $3.8B market cap according to Caplight. I'm sure ApeVue has an opinion on valuation. If Plaid is worth $3.8B+ what does that mean for Envestnet | Yodlee if spun off from Envestnet? Can they reach that type of valuation? If Plaid is setting the bar at $3.8B, down from their 2021 round at $13B+, it makes you wonder what their potential IPO looks like. Their new product, Layer, which speeds up the process for consumers who have previously linked an account, is their fastest-growing product in their history. The question is, can they continue to innovate and at the same time solve the frustration that many of their clients have with their core product offering. More here: https://lnkd.in/gwV3_cmk

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If you're offered a seat on a rocket ship, don't ask which seat... ...ask "are you sure it's going to fly???" --- Why I don't want to build a rocket ship, and would hesitate to join one: SpaceX is a huge success story today. The company is valued at >$100b with revenue of >$10b. But here's what it took to get off the ground - $100m of funding for Falcon 1 - 3 failed launches in 2006 and 2008 which nearly bankrupted Elon - $1.6b from NASA after a lucky successful fourth attempt Eric Schmidt was not thinking of a literal space company of course. "Rocketship" has become a metaphor for startups with ambitions of earth shattering meteoric growth. A close metaphor is the "moonshot." Google has a research division called X, the moonshot factory. Rockets sound cool, and going to the moon is a wonderful dream. But these are super high risk, super capital intensive, and super long term endeavors. Honestly, I don't want to try to build a rocket. And I don't want to try to go to the moon. I admire the founders and operators who go after these outcomes. It takes the very best, and it takes the life out of them. Elon was "waking from nightmares, screaming, and in physical pain" because of the stress of the failed launches and Tesla failing to secure financing before the big break in 2008. Now, he is the richest man on earth. That's not the life I want. In being an entrepreneur and founding a company, I want to work harder than most, take more risk than most, build something from nothing and change the lives of customers for the better with a product I built and own. Kondo is a small plane we're trying to build (while flying it). Hopefully our plane will be a little better, cheaper, faster than other planes. And then we can get a few more and carry more passengers. If going from LinkedIn's DM inbox to Kondo feels anything like switching airlines from American or United to Qatar Airways or Singapore Airlines, we've done our job.

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https://bit.ly/4bsJyjT // "Over the past 24 hours, I’ve put Copilot Workspace through some of its paces. I tried to have it build a large, complex feature on its own, but I also asked it to do smaller, better-defined tasks. My goal was to see what I could ask of it, what kinds of tasks it could handle, and when I might choose to use this instead of ChatGPT. The short answer is: This kind of product is the future of programming. The long answer is below. "

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Thank you to Jason Lengstorf for talking with me at CityJS Conferences about reducing stress for #softwareengineers, and sharing practical tips for advancing a coding career. + Get a sneak peek at "Web Lunch", Jason's latest series. Link down below!👇

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Why Does Golang Use Tabs Instead of Spaces? I'm not kidding. Since LinkedIn is now essentially a "social network" and everyone is talking about non-technical issues, I thought I bring up a "religious technical issue". Go (a.k.a. Golang) was developed by three guys at Google. Two of those guys were involved in Unix and C at Bell Labs. (Brian Kernighan and Dennis Ritchie). There has for a long time (pretty much forever) been a big war between those who believe tabs should be used to represent whitespace in programming languages and those who believe only spaces should be used. There is one issue that makes this important for correctness and ease of use with a large group of diverse programmers (different editing tools, computer systems, etc.) That is, how to represent whitespace. Those who want to use tabs say it lets you easily tab over to the place where you want to be those who use spaces worry about the fact that some people might like their tabstop (or tabwidth) set to 2, 4, 8, or some other size. For instance, when writing complex code, it is not unusual to have to indent 6-8 levels. When doing this sort of programming, tabstops of 8 just plain do not work. And, if the tabstop is set to 8 and you want to indent by 4, then you get cases where you have <space><space><space><space> because using a <tab> would take you to position 8 and you might be at 4, 12, 20, etc. (8, 16, 25, etc. are no problem for tab size of 8). So, we have a tradeoff. If 8 were acceptable, I could probably live with using <tab>, but after the first couple of years of programming, the number of people who use indentation of 8 for high-level programming is small. Most people use 2, 3, or 4. (Not so many use 3, but I've seen it.) So, this is the problem: 1. Google's official coding rules have been leaked (and I've heard them). They say "use spaces". The say it for the reason I like spaces. SPACES DO NOT CHANGE like <tabs> do when you switch the tabstop (width). 2. Go (Golang) has an auto formatting tool (https://go.dev/blog/gofmt) which use heavily used by the Go community. That tool formats to <tab> with a default of 8. How is this! Will someone please explain how a large, worldwide programming community could have decided to put tabstops of size 8 in their code repositories by default?

In the 0-1 phase of business building there seems to be a fine line between being scrappy/resourceful and being "penny wise and pound foolish." While this has always been an especially delicate balance to strike for bootstrapped founders, it seems to be a hot button topic amongst venture backed startups in light of the current funding environment (and focus on getting to breakeven). I'm glad to see more founders taking expenses (overall) more seriously, but I also believe it's possible to swing too far to the other extreme; hoarding resources to preserve runway and optionality (at the expense of taking risks which will always be required to build anything of significance). While there are no "right" answers on how to strike this balance, I think having folks you can process this with is helpful (sometimes we need to just get out of our own heads). This may be your investors, board members, advisors, or just close founder friends. Thoughts?

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Should You Offer Sign-On Bonuses? 75% of Later-Stage Companies Do vs. 33% of Seed-Stage Companies During the low interest era, sign-on bonuses became commonplace. At its peak, Facebook/Meta gave $100k sign-on bonuses to newgrad software engineers. $100k! “Close candidates faster with point-in-time payouts that don’t disrupt compa ratios.” Right? Well, sort of. The danger with sign-on bonuses is that they quickly become a slippery slope with some unfortunate consequences over time. Here are three: => Recruiters begin to normalize sign-on bonuses as a crutch => This means that sign-on bonuses quickly become expensive and difficult to forecast over time => Culturally speaking, employees who signed without receiving a sign-on bonus can feel slighted once they inevitably learn about their colleagues who received bonuses So, how common are sign-on bonuses these days? And do they make sense for your company? __________________ 𝗧𝗵𝗲 𝗳𝗶𝗻𝗱𝗶𝗻𝗴𝘀: Pave recently established a partnership with Greenhouse to be able to surface “offer data” benchmarks at scale. Today, let’s take a look at how common sign-on bonuses are by stage. To do this, we ran an analysis on 50,000+ offers from Greenhouse’s customer base over the trailing 18 months. Our data science team looked at accepted Greenhouse offers to IC (P1 → P6) and M (M3 → M6) level employees over the last 18 months and calculated the percentage of companies that included a sign-on bonus in at least one offer. Results are broken out by company headcount. 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 => 𝘁𝗵𝗲 𝗹𝗮𝘁𝗲𝗿 𝘀𝘁𝗮𝗴𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘁𝗵𝗲 𝗺𝗼𝗿𝗲 𝗰𝗼𝗺𝗺𝗼𝗻 𝘀𝗶𝗴𝗻-𝗼𝗻 𝗯𝗼𝗻𝘂𝘀𝗲𝘀 𝗯𝗲𝗰𝗼𝗺𝗲. ✅ 1-100 employees: 32% of companies used sign-on bonuses ✅ 101-200 employees: 39% ✅ 201-500 employees: 43% ✅ 501-1,000 employees: 70% ✅ 1,001+ employees: 75% __________________ How do you weigh the pros and cons of sign-on bonuses at your company? #pave #signonbonus #benchmarks

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AI is going to reduce the cost of code generation dramatically. Left unchecked, this will cause an exponential increase in cost of observability and root cause analysis. We believe that AI will come to the rescue, and Agent SREs will be a $100B category. The wrinkle is that there are already 50+ start-ups vying for the Agent SRE crown. If you have ideas on what will define the category winner, do ping me and/or Jaya Gupta https://lnkd.in/gyAnWarK

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