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urn string	text string	type string	firstName string	lastName string	numImpressions int64	numViews int64	numReactions int64	numComments int64	numShares int64	numVotes int64	numEngagementRate float64	hashtags string	createdAt (TZ=America/Los_Angeles) string	link string
urn:li:activity:7437904645592862720	"𝘐 𝘥𝘰𝘯'𝘵 𝘬𝘯𝘰𝘸 𝘩𝘰𝘸 𝘢𝘯𝘺 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘸𝘰𝘳𝘬𝘴" Our lead data scientist had created a service to support multi-modal embedding and retrieval from a vector database and a host of other functionality we needed for classifying and analyzing videos. This was a greenfield project with an aggressive timeline. He was the first person to introduce me to Opus. He's what I consider an AI expert with deep mathematical expertise and a PhD. Still, there was no way a human could possibly understand the amount of code that Claude produced. Not because it was bad. Just because there was so much of it. At one point he mentioned 𝗶𝘁 𝘄𝗼𝘂𝗹𝗱 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘁𝗮𝗸𝗲 𝘄𝗲𝗲𝗸𝘀 𝘁𝗼 𝗳𝘂𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲 𝗰𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝗱𝗲𝘃𝗲𝗹𝗼𝗽 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝘁𝗲𝘀𝘁 𝘀𝘂𝗶𝘁𝗲. And that got me thinking. 𝙄𝙨 𝙝𝙞𝙜𝙝-𝙡𝙚𝙫𝙚𝙡 𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙚𝙣𝙤𝙪𝙜𝙝? In some ways, yes. Developers work on systems we didn’t build MOST of the time. Understanding the architecture and intent is usually enough to move things forward. But eventually someone needs to know where the bodies are buried. Code breaks. Edge cases appear. Systems evolve. And when that happens, someone has to understand the service deeply enough to debug it. Which makes me wonder: 𝘈𝘳𝘦 𝘸𝘦 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘣𝘦𝘤𝘰𝘮𝘪𝘯𝘨 𝘮𝘰𝘳𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘷𝘦… 𝘖𝘳 𝘢𝘳𝘦 𝘸𝘦 𝘫𝘶𝘴𝘵 𝘬𝘪𝘤𝘬𝘪𝘯𝘨 𝘵𝘩𝘦 𝘤𝘢𝘯 𝘥𝘰𝘸𝘯 𝘵𝘩𝘦 𝘳𝘰𝘢𝘥? Shipping faster now, while pushing the real understanding cost into the future.	TEXT	Brian	Jenney	641	0	9	2	1	0	0.018721	null	2026-03-12 09:57:37	https://www.linkedin.com/feed/update/urn:li:activity:7437904645592862720
urn:li:activity:7437535914496765952	You don’t need to learn how to code anymore. All you need to learn is: • How to use Git • How deployment works • Lambdas vs EC2 • AWS vs GCP vs Vercel • SQL vs NoSQL databases • Foreign key relationships • Indexes and query performance • LEFT JOIN vs INNER JOIN • Caching • Environment variables • Authentication flows • Debugging production issues • CI/CD pipelines …and then you just tell the AI what code to write. It’s just that simple.	TEXT	Brian	Jenney	12,346	0	94	18	2	0	0.009234	null	2026-03-11 09:32:24	https://www.linkedin.com/feed/update/urn:li:activity:7437535914496765952
urn:li:activity:7437531279597735936	The story is deeper than the headline. At least one Stanford economist calculated a productivity increase of 2.7% compared to this time last year. Others see modest gains. The 6,000 CEOs who were surveyed have seen basically little to no impact in their own orgs. Either way - this isn't the dystopian reality we were promised. Don't worry - it's just 12 months away 😉	IMAGE	Brian	Jenney	1,568	0	7	5	2	0	0.008929	null	2026-03-11 09:13:59	https://www.linkedin.com/feed/update/urn:li:activity:7437531279597735936
urn:li:activity:7437183039337803776	Outside of being a software developer, I also run a small business. Here's where I use AI and where I absolutely do NOT. ✅ 𝗨𝗽𝗱𝗮𝘁𝗶𝗻𝗴 𝗰𝘂𝗿𝗿𝗶𝗰𝘂𝗹𝘂𝗺 - tedious updates are now trivial. This has saved me hundreds of hours ✅ 𝗟𝗮𝗻𝗱𝗶𝗻𝗴 𝗽𝗮𝗴𝗲𝘀 - I do not have a design eye. A landing page is a great target for Claude to create on its own (like this one: https://parsity.io/ai-dev) ✅ 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 - I built a clone of myself, with access to thousands of examples of my writing (including this post) that helps me brainstorm and create articles/posts/transcripts H͟e͟r͟e͟'͟s͟ ͟w͟h͟e͟r͟e͟ ͟I͟ ͟a͟v͟o͟i͟d͟ ͟A͟I͟:͟ ❌ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 - yeah, I could create a tool to do this but it seems cold and the error rate is too high. Human mentorship is our secret sauce. I cannot imagine automating this. ❌ 𝗣𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝗻𝗴 - first off, LinkedIn will ban your ass if they catch you automating your messages. I also straight up don't trust an agent to hold a reasonable convo with a potential customer. We've seen massive companies fail spectacularly with this. I'm not going to beat them. ❌ 𝗘𝗺𝗮𝗶𝗹𝘀 - I played with the idea of having an agent read through my inbox and reply to emails. Again - the potential for error is just too high. We aren't drowning in emails either. The juice just isn't worth the squeeze. I'll continue to look for areas we can squeeze out productivity without sacrificing quality or our human touch. If you're a business owner, I'm curious where you're finding the most value when it comes to AI.	TEXT	Brian	Jenney	4,876	0	21	3	0	0	0.004922	null	2026-03-10 10:10:12	https://www.linkedin.com/feed/update/urn:li:activity:7437183039337803776
urn:li:activity:7436803318062149632	250 applications and barely an interview. Bryan Peterson experienced what I hear from a lot of developers on the job hunt nowadays. He updated his strategy and has had 16 interviews from 58 applications. 🤯 I met Bryan in Reno, NV last year and I saw the proof. We sat down to chat about technical interviews, surprising ways he's failed and succeeded in the job search and a few mental shifts that can save your sanity when it feels like you're sending applications out into the void. https://lnkd.in/gmwc7ikU	ARTICLE	Brian	Jenney	2,818	0	19	2	0	0	0.007452	null	2026-03-09 09:01:20	https://www.linkedin.com/feed/update/urn:li:activity:7436803318062149632
urn:li:activity:7436171946171498496	Last night I was riding through San Francisco in a Waymo with Alex Lau (author of Keep Calm, Code On), passing giant AI billboards promising to automate us out of jobs. Naturally the conversation turned to the future of software engineers. Neither of us claims to know the answer. But one thing is clear: the job is changing faster than most people realize. I’ve started noticing a few predictable levels of AI leverage developers move through. Most people are stuck at level one. The interesting opportunities are in the levels beyond that. Here’s what those look like.	ARTICLE	Brian	Jenney	2,533	0	15	2	0	0	0.006711	null	2026-03-07 14:12:29	https://www.linkedin.com/feed/update/urn:li:activity:7436171946171498496
urn:li:activity:7435392216933482496	I was demoing an AI agent (𝘳𝘦𝘢𝘭𝘭𝘺 𝘢 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸 𝘣𝘶𝘵, 𝘵𝘰𝘮𝘢𝘵𝘰 𝘵𝘰𝘮𝘢𝘵𝘰) — that broke at the worst possible time. Our CEO asked it to find influencers in our database that charged less than $1000 per post. No results. I had tried this scenario so many times before demo day. What happened?! When I inspected the logs, I saw our agent was sometimes parsing 1K as $1 or $10 instead of $1000 😅 So the agent searched the database and confidently concluded: 🤖 “𝘕𝘰 𝘪𝘯𝘧𝘭𝘶𝘦𝘯𝘤𝘦𝘳𝘴 𝘧𝘰𝘶𝘯𝘥.” A tiny interpretation mistake that completely broke the outcome. This is something I’ve noticed a lot building AI agents: They fail in subtle, unexpected ways like unit conversions, formatting quirks and edge cases you don't think about. In our case, the fix wasn’t fancy. We added a regex parser to extract the price and compare it against what the AI produced. LLM observability tools like Langsmith allowed us to track these differences and determine if tweaks to our system prompt improved accuracy. Not sexy. But it’s a good reminder that when building agents, a lot of the work is anticipating the weird ways they might be 𝘢𝘭𝘮𝘰𝘴𝘵 right.	TEXT	Brian	Jenney	937	0	14	9	1	0	0.025614	null	2026-03-05 10:34:07	https://www.linkedin.com/feed/update/urn:li:activity:7435392216933482496
urn:li:activity:7435022923012128768	𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗮𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁. You need a workflow. Most teams asking for an “agent” really want something like this: 𝚄𝚜𝚎𝚛 𝚛𝚎𝚚𝚞𝚎𝚜𝚝 → 𝚌𝚕𝚊𝚜𝚜𝚒𝚏𝚢 𝚒𝚗𝚝𝚎𝚗𝚝 → 𝚛𝚘𝚞𝚝𝚎 𝚝𝚘 𝚝𝚑𝚎 𝚛𝚒𝚐𝚑𝚝 𝚝𝚘𝚘𝚕 → 𝚛𝚎𝚝𝚞𝚛𝚗 𝚝𝚑𝚎 𝚛𝚎𝚜𝚞𝚕𝚝. That’s a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄. Anthropic knows this. LangGraph knows this too. “Agents” get all the publicity but workflows get shipped. One of the simplest ways to build this is with the 𝗿𝗼𝘂𝘁𝗲𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 — where an LLM looks at a request and decides which path your system should take (search, API call, database query, etc). Simple. Predictable. 𝙔𝙤𝙪 𝙘𝙖𝙣 𝙜𝙧𝙖𝙗 𝙖 𝙨𝙢𝙖𝙡𝙡 𝙥𝙧𝙤𝙟𝙚𝙘𝙩 𝙩𝙤 𝙛𝙤𝙧𝙘𝙚 𝙮𝙤𝙪𝙧 𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜: https://lnkd.in/gz2Q9hSf	TEXT	Brian	Jenney	798	0	14	3	0	0	0.021303	null	2026-03-04 10:06:40	https://www.linkedin.com/feed/update/urn:li:activity:7435022923012128768
urn:li:activity:7435012931534077952	Everybody loves to hate LinkedIn. “𝘐𝘵’𝘴 𝘤𝘳𝘪𝘯𝘨𝘦.” “𝘐𝘵’𝘴 𝘢𝘭𝘭 𝘈𝘐 𝘴𝘭𝘰𝘱.” “𝘐’𝘮 𝘯𝘰𝘵 𝘱𝘰𝘴𝘵𝘪𝘯𝘨.” Cool. But if your goal is a software engineering job, you don’t get to opt out... yet. LinkedIn isn’t for your friends or your family. It’s for recruiters. And recruiters don’t want to take a chance on you. So your job is simple: 𝗱𝗲-𝗿𝗶𝘀𝗸 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 and leave evidence you can actually do the work. A few of the lowest-hanging fixes I see constantly (especially with junior devs): • 𝗦𝘁𝗼𝗽 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 “𝗷𝘂𝗻𝗶𝗼𝗿” or “aspiring.” You’re a developer. • 𝗨𝘀𝗲 𝘀𝘁𝗿𝗼𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲: 𝘣𝘶𝘪𝘭𝘵, 𝘴𝘩𝘪𝘱𝘱𝘦𝘥, 𝘭𝘦𝘥, 𝘪𝘮𝘱𝘳𝘰𝘷𝘦𝘥 𝘟 𝘣𝘺 𝘠 — not vague “worked on a team.” • 𝗟𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (projects, freelance, volunteer) so you don’t look like a career pivot with zero proof. I broke all of this down, plus a simple strategy for getting more inbound opportunities without living on LinkedIn in this latest episode of Develop Yourself: https://lnkd.in/gvrnv9HC 𝘗𝘚 - 𝘺𝘰𝘶 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘯𝘦𝘸 𝘷𝘪𝘥𝘦𝘰 𝘧𝘰𝘳𝘮𝘢𝘵?	ARTICLE	Brian	Jenney	4,469	0	46	18	1	0	0.014545	null	2026-03-04 09:26:58	https://www.linkedin.com/feed/update/urn:li:activity:7435012931534077952
urn:li:activity:7434651315332444160	It's honestly kinda wild how much coding you still need to know if you want to Vibe Code an app and have no technical background. I wrote this article for non-devs who are interested in launching an app but don't want to learn to code.	ARTICLE	Brian	Jenney	1,734	0	16	1	1	0	0.010381	null	2026-03-03 09:30:02	https://www.linkedin.com/feed/update/urn:li:activity:7434651315332444160
urn:li:activity:7434648156744740864	I'm a software developer and I own a small business. I'm not replacing a single person with AI. Parsity has had a thin staff of mentors and an assistant for years. Most of our processes were automated since before I took over and well before anyone knew what Claude was. I don't see a world where I have an AI assistant reaching out to prospective students, writing our weekly newsletter, grading work or "mentoring" people. I'm not trying to take some moral high ground here either. Parsity depends on humans to instruct, mentor and create curriculum. Believe it or not, but humans typically want to interact with... other humans! Shaving away people to save money while delivering an inferior experience makes 0 sense to me.	TEXT	Brian	Jenney	5,025	0	88	18	1	0	0.021294	null	2026-03-03 09:17:29	https://www.linkedin.com/feed/update/urn:li:activity:7434648156744740864
urn:li:activity:7434284544440147968	While non-coders are busy arguing about whether or not AI replaces coders, consider learning these skills: 1. RAG 2. AI agent patterns 3. LLM ops 4. Testing and eval strategies for models There is a major opportunity a few layers beneath learning how to use Claude better. https://lnkd.in/gsNyXZkN	ARTICLE	Brian	Jenney	1,740	0	25	0	0	0	0.014368	null	2026-03-02 09:12:37	https://www.linkedin.com/feed/update/urn:li:activity:7434284544440147968
urn:li:activity:7433233332756193280	I was writing an article to help Vibe Coders ship something. It was immediately clear how much technical knowledge you still need to have outside coding to actually launch: • IDE set up • Cloud deployment • e2e tests (if you want to avoid regressions or bugs) • Github • Debugging Anything I missed?	TEXT	Brian	Jenney	1,240	0	19	6	0	0	0.020161	null	2026-02-27 11:35:29	https://www.linkedin.com/feed/update/urn:li:activity:7433233332756193280
urn:li:activity:7433198293301723136	This might lose me some business, but it needs to be said: You can't stack AI skills on top of shaky software skills. Before you join a program like https://parsity.io/ai-dev and dive into agents, RAG, or fine-tuning, you need real coding basics. Do this first: • 𝗚𝗲𝘁 𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: APIs, common design patterns and building and deploying full stack apps • 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗜 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲𝘀: Tokens, embeddings, context windows. If those concepts feel fuzzy, everything advanced will feel like magic. Here’s the truth: AI engineering isn’t a shortcut. It’s software development with more moving parts and a rule book that is still being written. If you don’t have a solid foundation you'll be building on a house of cards.	TEXT	Brian	Jenney	2,172	0	37	3	1	0	0.018877	null	2026-02-27 09:16:15	https://www.linkedin.com/feed/update/urn:li:activity:7433198293301723136
urn:li:activity:7432813999882293248	Before I was a software developer, I was a [redacted for legal reasons]. But before that, I honestly wanted to be a writer. Kinda still do. It's one of the many reasons I write so much. If you've been connected to me for a while, you also know I don't do sponsorships. Not because I have some moral stance on sponsorships, it's just that I have my own business that I shamelessly promote. Educative is a product I actually like, have paid for and used heavily when I first started studying for interviews. The thing that stands out about their platform is that it's text-first. I prefer text to video 9 times out of ten. Easier to digest == quicker to understand. They just published a short article I wrote about interviews in the AI era. I predict that your next interview is more likely to look like one I did recently than whatever you've seen in the past...	SHARE	Brian	Jenney	3,356	0	21	4	0	0	0.007449	null	2026-02-26 07:49:12	https://www.linkedin.com/feed/update/urn:li:activity:7432813999882293248
urn:li:activity:7432571818332368896	There’s a massive digital divide that is only getting wider - especially in cities like Oakland. When I bumped into Adamaka Ajaelo a few weeks ago in Oakland, CA I learned more about how she’s helping bridge that gap with Self-eSTEM. Adamaka has a highly technical background and teaches practical skills to youth and adults. Give her a follow if you fancy.	IMAGE	Brian	Jenney	1,166	0	24	2	0	0	0.022298	null	2026-02-25 15:46:51	https://www.linkedin.com/feed/update/urn:li:activity:7432571818332368896
urn:li:activity:7432271273872904192	"𝘏𝘦𝘭𝘱! 𝘞𝘦 𝘨𝘢𝘷𝘦 𝘢𝘯 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵 𝘢𝘤𝘤𝘦𝘴𝘴 𝘵𝘰 𝘰𝘶𝘳 𝘦𝘯𝘵𝘪𝘳𝘦 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘢𝘯𝘥 𝘴𝘰𝘮𝘦𝘰𝘯𝘦 𝘰𝘯 𝘰𝘶𝘳 𝘵𝘦𝘢𝘮 𝘫𝘶𝘴𝘵 𝘥𝘦𝘭𝘦𝘵𝘦𝘥 𝘢𝘭𝘭 𝘰𝘶𝘳 𝘶𝘴𝘦𝘳𝘴 𝘣𝘺 𝘢𝘤𝘤𝘪𝘥𝘦𝘯𝘵!" We’re going to save this fictional (or not-so-fictional) company in a live event and learn how using human-in-the-loop could have prevented this mess. If you’re curious about how to build AI agents in a practical way, you should join. If you don’t see a problem with giving AI tools total access to your data, then maybe this isn’t the event for you 😅	EVENT	Brian	Jenney	902	0	17	3	5	0	0.027716	null	2026-02-24 19:52:36	https://www.linkedin.com/feed/update/urn:li:activity:7432271273872904192
urn:li:activity:7432116119768834048	Did you know you can run Python in your NextJS app? I was so jealous when Ali Cagatay showed me his capstone project from Parsity's AI Accelerator program which included downloading the transcripts from popular YouTuber's so a user could "talk" with them through a chat interface. The library he was using is only available in Python. 😢 Turns out, NextJS now supports Python runtime. I have API routes in both Typescript and Python. I'm sure this irks many of you... A cron job in Python scrapes my most recent videos, chunks up the transcript and stores it in a vector database for easy searching and retrieval to help generate more content that sounds like me. I can't wait to see what people build in the next cohort that I can steal 😅	IMAGE	Brian	Jenney	926	0	13	2	0	0	0.016199	null	2026-02-24 09:36:04	https://www.linkedin.com/feed/update/urn:li:activity:7432116119768834048
urn:li:activity:7431750092530860032	Cheating in interviews is rampant. It's gotten so bad that there's a cottage industry dedicated to preventing cheaters and detecting AI usage. Fardeen Khimani 😇 has a different approach: - no Leetcode - unlimited AI usage - no marathon interview rounds - faster hiring based on challenges that reflect the actual work He has some pretty spicy takes on cheating with AI, the death of the whiteboard interview and why most companies are behind the times with their process. https://lnkd.in/gmZXcGdy	ARTICLE	Brian	Jenney	8,083	0	48	7	5	0	0.007423	null	2026-02-23 09:21:37	https://www.linkedin.com/feed/update/urn:li:activity:7431750092530860032
urn:li:activity:7431744752867254273	If you’re a web developer, you’re not far from AI engineering. But the shift isn’t (just) about using Claude better. Here’s a practical roadmap. 𝟭. 𝗔𝗣𝗜-𝗙𝗶𝗿𝘀𝘁 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Start calling models via API. Learn how to structure prompts programmatically, manage context windows, constrain outputs, and control behavior through parameters. The real skill isn’t “prompting.” It’s designing the inputs so the model has the right information at the right time. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁: Call LLMs directly via API. Experiment with system messages, structured prompts, output schemas, temperature, and response formats inside real applications. 𝟮. 𝗟𝗟𝗠 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗜𝗻𝘁𝘂𝗶𝘁𝗶𝗼𝗻) You don’t need deep math, but you do need a mental model. Understand, at a high level, how transformers, embeddings, attention, and feed-forward layers work. This sharpens your intuition about what models can and can’t do and helps you avoid hype-cycle nonsense. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁: Build a tiny language model in Python with PyTorch. Or watch 3Blue1Brown’s transformer series and the first few episodes of his linear algebra series. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝘀, 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 & 𝗧𝗼𝗼𝗹 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 Learn the difference between deterministic workflows and autonomous agents. Study patterns like ReAct, human-in-the-loop systems, orchestrator patterns, and tool calling — and when each makes sense. More autonomy isn’t always better. Reliability is a design decision. https://lnkd.in/gz2Q9hSf 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁: Build a small agent that uses tools. Then refactor it into a structured workflow and compare reliability and control. 𝟰. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) Most real AI products leverage retrieval systems. Learn how to chunk documents, generate embeddings, and design retrieval pipelines for internal knowledge, semantic search, and context-aware applications. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁: Build a simple RAG pipeline on your own documentation or notes. https://lnkd.in/d6Pzfcg6 𝟱. 𝗟𝗟𝗠 𝗢𝗽𝘀 (𝗘𝘃𝗮𝗹𝘀 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆) AI systems don’t fail loudly — they drift. (𝘙𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘵𝘩𝘦 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘤𝘦 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘎𝘗𝘛-4 𝘢𝘯𝘥 5?) You need testing strategies, evaluation datasets, and observability to understand performance over time. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁: Create a test suite for the agents you built earlier. Add tools like Helicone or LangSmith to introduce observability and structured evaluation. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝘂𝘀𝗲𝗿 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱𝗲𝗿.	TEXT	Brian	Jenney	1,558	0	27	2	2	0	0.019897	null	2026-02-23 09:00:24	https://www.linkedin.com/feed/update/urn:li:activity:7431744752867254273
urn:li:activity:7431022202671677440	What software developers can learn from an insecure fictional character from the 19th century. And why it’s especially relevant in the AI hype cycle.	ARTICLE	Brian	Jenney	1,761	0	15	2	0	0	0.009654	null	2026-02-21 09:09:14	https://www.linkedin.com/feed/update/urn:li:activity:7431022202671677440
urn:li:activity:7430302390148485120	In 2024, I remember sitting in the office, nervous what the CEO might think of me using ChatGPT to write some code. Maybe he'd re-consider if I was the right fit? That I wasn't a "real" programmer. In less than 2 years the tables have completely flipped. If you're NOT using AI coding tools nowadays, it's a bright red flag for most(?) companies. Your next interview is likely to reflect this new reality. I break down a recent coding interview that allowed AI tools... and how that didn't make it any easier: https://lnkd.in/gnDt3i7q	ARTICLE	Brian	Jenney	5,636	0	21	1	1	0	0.004081	null	2026-02-19 09:28:58	https://www.linkedin.com/feed/update/urn:li:activity:7430302390148485120
urn:li:activity:7429932716353208320	When I built the curriculum and homework for Parsity's AI Cohort, I didn't do it on vibes. I took a mix of what I learned working at 2 different AI startups as a full stack developer and the skills I keep seeing in the market. It's the reason I decided against MCP, heavy math and a diving deeper into fine-tuning. I stand by my decision to keep the codebase in Typescript, for now. The concepts translate easily across stacks and the amount of material out there for TS devs when it comes to AI is shockingly low. 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗶𝘀 𝗽𝗿𝗲𝘁𝘁𝘆 𝘀𝗶𝗺𝗽𝗹𝗲: make you the goto person in your organization when it comes to AI integration OR help you transition into an full stack AI engineer role. You can apply here: https://parsity.io/ai-dev	IMAGE	Brian	Jenney	1,123	0	16	0	0	0	0.014248	null	2026-02-18 09:00:01	https://www.linkedin.com/feed/update/urn:li:activity:7429932716353208320
urn:li:activity:7429574081773166592	It’s not just you. Expectations around software delivery have gotten unhinged. For years we said, “𝘈𝘯𝘺𝘰𝘯𝘦 𝘤𝘢𝘯 𝘭𝘦𝘢𝘳𝘯 𝘵𝘰 𝘤𝘰𝘥𝘦.” Now anyone can generate code. For better or for worse. 𝗕𝗨𝗧, generating code isn’t the same as building software. A feature that works on localhost isn’t production-ready. When non-technical leaders can ship something that "works," it creates the illusion that software development was always just typing. You might be dealing with: • Eroded trust ("𝘉𝘶𝘵 𝘪𝘵 𝘸𝘰𝘳𝘬𝘦𝘥 𝘸𝘩𝘦𝘯 𝘐 𝘳𝘢𝘯 𝘪𝘵 𝘭𝘰𝘤𝘢𝘭𝘭𝘺.") • Inflated expectations ("𝘐 𝘣𝘶𝘪𝘭𝘵 𝘵𝘩𝘪𝘴 𝘪𝘯 𝘢 𝘸𝘦𝘦𝘬𝘦𝘯𝘥 𝘧𝘳𝘰𝘮 𝘴𝘤𝘳𝘢𝘵𝘤𝘩!") • The belief that typing speed was the bottleneck all along. I think there's also a lot of opportunity in this odd stage we're in. If you take the time to articulate why that "simple service" requires tests, additional infra and a rollback strategy - then you can position yourself as a leader on the team. Obviously, your mileage will vary. 𝙏𝙝𝙞𝙨 𝙬𝙚𝙚𝙠𝙚𝙣𝙙, 𝙄'𝙡𝙡 𝙗𝙚 𝙬𝙧𝙞𝙩𝙞𝙣𝙜 𝙖𝙗𝙤𝙪𝙩 𝙝𝙤𝙬 𝙮𝙤𝙪 𝙘𝙖𝙣 𝙜𝙚𝙩 𝙮𝙤𝙪𝙧 𝘾𝙮𝙧𝙖𝙣𝙤 𝘿𝙚 𝘽𝙚𝙧𝙜𝙚𝙧𝙖𝙘 𝙢𝙤𝙢𝙚𝙣𝙩 𝙖𝙨 𝙖 𝙙𝙚𝙫𝙚𝙡𝙤𝙥𝙚𝙧 𝙞𝙣 𝙩𝙝𝙚 𝘼𝙄 𝙚𝙧𝙖 𝙖𝙣𝙙 𝙬𝙝𝙮 𝙩𝙝𝙞𝙨 𝙤𝙥𝙥𝙤𝙧𝙩𝙪𝙣𝙞𝙩𝙮 𝙬𝙤𝙣'𝙩 𝙡𝙖𝙨𝙩. https://lnkd.in/gmEZY7K8	TEXT	Brian	Jenney	1,763	0	21	5	0	0	0.014748	null	2026-02-17 09:14:55	https://www.linkedin.com/feed/update/urn:li:activity:7429574081773166592
urn:li:activity:7429199815932071937	Hiring is obviously broken. To make it worse, you're using the same tactics that haven't worked since 2019. 𝗦𝗼𝗺𝗲 𝗵𝗮𝗿𝘀𝗵 𝘁𝗿𝘂𝘁𝗵𝘀: - Your resume is increasingly less important - Luck is a factor no one wants to admit - Mass applying is like playing the lotto - LinkedIn is a social media site pretending to be a job board 𝗦𝗼𝗺𝗲 𝘀𝘁𝘂𝗳𝗳 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝘀𝘁𝗮𝗿𝘁 𝗱𝗼𝗶𝗻𝗴: - Get 500 connections on LinkedIn to be more discoverable - Remove any mention of junior/aspiring/student from your profile - Don't apply for only junior roles - let the market decide - Do a BFS of your network to find hidden jobs - Build in public - this is uncomfortable and effective (𝘺𝘰𝘶'𝘭𝘭 𝘢𝘭𝘴𝘰 𝘣𝘦 𝘮𝘰𝘳𝘦 𝘷𝘪𝘴𝘪𝘣𝘭𝘦 𝘪𝘧 𝘳𝘦𝘤𝘳𝘶𝘪𝘵𝘦𝘳𝘴 𝘢𝘳𝘦 𝘶𝘴𝘪𝘯𝘨 𝘧𝘪𝘭𝘵𝘦𝘳𝘴 𝘧𝘰𝘳 𝘢𝘤𝘵𝘪𝘷𝘪𝘵𝘺 😉) I'm not saying this is easy or fair. If you've already gone to a coding bootcamp and are looking to land your first role - I'd personally work with David Roberts If you're at the beginning of your journey and realize it's going to take more than coding skills to break in, join Parsity https://parsity.io	TEXT	Brian	Jenney	11,385	0	59	14	1	0	0.0065	null	2026-02-16 08:27:43	https://www.linkedin.com/feed/update/urn:li:activity:7429199815932071937
urn:li:activity:7428119230572728320	Apocalypse marketing is the most effective AI growth strategy right now. Step 1: CEO predicts white-collar job collapse in X months. Step 2: Internet panics. Step 3: Engagement explodes. Yale Budget Lab’s analysis of the labor market so far shows no clear sign that AI exposure is causing large employment shifts… yet. So either: - executives know something the data doesn’t… - or fear is great marketing. Sometimes it feels like we’re using two different AIs.	TEXT	Brian	Jenney	931	0	12	8	0	0	0.021482	null	2026-02-13 08:53:52	https://www.linkedin.com/feed/update/urn:li:activity:7428119230572728320
urn:li:activity:7427791445362733057	Imagine being a junior developer right now. You have CEOs of major tech companies telling you that no one will write code in the next few months. Every other tech influencer is showing off their bespoke workflow with multiple agents. Is it any wonder that more early career developers are over-relying on AI to do their work? Turns out, there is a very real cost to doing this, based on a recent study from Anthropic. If you don't want to turn into a prompt-squirrel, I outline some things you might want to consider...	ARTICLE	Brian	Jenney	11,084	0	49	10	1	0	0.005413	null	2026-02-12 11:11:22	https://www.linkedin.com/feed/update/urn:li:activity:7427791445362733057
urn:li:activity:7427768581465186305	𝘛𝘩𝘦 𝘤𝘰𝘥𝘪𝘯𝘨 𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘌𝘯𝘨𝘭𝘪𝘴𝘩! 𝘈𝘐 𝘸𝘪𝘭𝘭 𝘳𝘦𝘱𝘭𝘢𝘤𝘦 92.8% 𝘰𝘧 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴 𝘣𝘺 [𝘯𝘦𝘹𝘵 𝘦𝘢𝘳𝘯𝘪𝘯𝘨𝘴 𝘤𝘢𝘭𝘭]! 𝘠𝘰𝘶 𝘸𝘰𝘯'𝘵 𝘣𝘦 𝘳𝘦𝘱𝘭𝘢𝘤𝘦𝘥 𝘣𝘺 𝘈𝘐, 𝘺𝘰𝘶'𝘭𝘭 𝘣𝘦 𝘳𝘦𝘱𝘭𝘢𝘤𝘦𝘥 𝘣𝘺 𝘴𝘰𝘮𝘦𝘰𝘯𝘦 𝘶𝘴𝘪𝘯𝘨 𝘈𝘐! Sufficiently rage-baited? I don't have a crystal ball and I don't know what the future of AI means for code authors. I do know this: In the last 12 months I went from knowing nothing about AI/LLMs to using vector databases to create RAG applications and building data pipelines to ingest tons of data to train AI models. This has been a ton of fun and seems to have made me a hotter commodity on the job market than I have been in quite some time. Because I like you - I've created a small project to teach you the basics of RAG, a popular and useful way to retrieve relevant info for LLMs. Grab it here: https://lnkd.in/d6Pzfcg6	TEXT	Brian	Jenney	3,380	0	37	2	0	0	0.011538	null	2026-02-12 09:40:31	https://www.linkedin.com/feed/update/urn:li:activity:7427768581465186305
urn:li:activity:7427044451094585345	This guy AIs. I've met plenty of "AI experts" who know a lot about theory but don't know how to build or think through practical problems that map to business use cases. Tad didn't ask me to write this but with so much misinformation and snake oil being peddled, we need more Tad's out there.	SHARE	Brian	Jenney	602	0	10	2	1	0	0.021595	null	2026-02-10 09:43:04	https://www.linkedin.com/feed/update/urn:li:activity:7427044451094585345
urn:li:activity:7426669081438720000	Most AI projects start off with: - “Which model should we use?” - “LangGraph vs CrewAI vs Vercel AI SDK?” - “Python or Typescript?” When they should start off with: - “Where’s the data?”	ARTICLE	Brian	Jenney	1,280	0	11	4	0	0	0.011719	null	2026-02-09 08:51:29	https://www.linkedin.com/feed/update/urn:li:activity:7426669081438720000
urn:li:activity:7426351032420573184	❌ 10 prompts to 10x your workflow! ❌ Launch your first agent with ZERO code (or planning)! ❌ How use OpenClaw to drain your bank account! Sorry AI bros - this post isn't meant for you. Just wrapped up 4 weeks of building a complex RAG application with a small group of developers. The results were well beyond what I expected. They made projects and products which have some serious value. Before we launch the next cohort, I want to do a live session for builders. For Typescript devs who want to party with AI - I think you'll find this valuable.	POLL	Brian	Jenney	2,083	0	10	1	0	30	0.019683	null	2026-02-08 11:47:41	https://www.linkedin.com/feed/update/urn:li:activity:7426351032420573184
urn:li:activity:7425565696803287040	We wrapped our first AI Cohort with a special guest, John Crickett , who showed us how to build: • an agent that writes code • and another one that impersonates a certain over-posting, mildly cringey LinkedIn influencer (me) In the middle of the workshop, John said something that stuck with us: “𝘼𝙄 𝙙𝙤𝙚𝙨𝙣’𝙩 𝙧𝙚𝙢𝙤𝙫𝙚 𝙩𝙝𝙚 𝙣𝙚𝙚𝙙 𝙛𝙤𝙧 𝙙𝙚𝙫𝙚𝙡𝙤𝙥𝙚𝙧𝙨 — 𝙞𝙩 𝙨𝙝𝙞𝙛𝙩𝙨 𝙩𝙝𝙚 𝙟𝙤𝙗 𝙩𝙤𝙬𝙖𝙧𝙙 𝙩𝙝𝙞𝙣𝙠𝙞𝙣𝙜, 𝙨𝙘𝙤𝙥𝙞𝙣𝙜, 𝙖𝙣𝙙 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣-𝙢𝙖𝙠𝙞𝙣𝙜.” Then he followed it with this: “𝘼 𝙡𝙤𝙩 𝙤𝙛 𝙥𝙚𝙤𝙥𝙡𝙚 𝙖𝙧𝙚 𝙣𝙤𝙩 𝙩𝙝𝙞𝙣𝙠𝙞𝙣𝙜 — 𝙩𝙝𝙚𝙮 𝙟𝙪𝙨𝙩 𝙖𝙘𝙘𝙚𝙥𝙩 𝙚𝙫𝙚𝙧𝙮𝙩𝙝𝙞𝙣𝙜 𝙩𝙝𝙚 𝘼𝙄 𝙨𝙪𝙜𝙜𝙚𝙨𝙩𝙨. 𝙏𝙝𝙚 𝙥𝙧𝙤𝙟𝙚𝙘𝙩 𝙨𝙘𝙤𝙥𝙚 𝙗𝙖𝙡𝙡𝙤𝙤𝙣𝙨. 𝙏𝙝𝙖𝙩’𝙨 𝙣𝙤𝙩 𝙨𝙤𝙛𝙩𝙬𝙖𝙧𝙚 𝙚𝙣𝙜𝙞𝙣𝙚𝙚𝙧𝙞𝙣𝙜.” TBH, the hard part about building AI agents isn’t prompts. In this cohort, most developers spent far more time: – gathering and cleaning data – deciding what the system should actually do – cutting scope to deliver a useful product in a short time frame The real skill wasn’t “using AI.” It was engineering judgment. And in just a few weeks, this group built some seriously impressive stuff. We’re running it again. 𝗡𝗲𝘅𝘁 𝗔𝗜 𝗗𝗲𝘃 𝗖𝗼𝗵𝗼𝗿𝘁 𝘀𝘁𝗮𝗿𝘁𝘀 𝗶𝗻 𝗔𝗽𝗿𝗶𝗹. https://parsity.io/ai-dev	IMAGE	Brian	Jenney	2,260	0	39	15	0	0	0.023894	null	2026-02-06 07:47:02	https://www.linkedin.com/feed/update/urn:li:activity:7425565696803287040
urn:li:activity:7424865775259389952	Last year I took a week off work and traveled to beautiful (kinda) Reno, NV to create a new AI curriculum for Parsity. That week I got a chance to speak to Alex Hormozi on his show, met a connection from LinkedIn in real life and gambled away my life savings! Fun times. At the time, I was working at my 2nd AI start-up and learning things like RAG, agents, workflows, tool-calling and LLM-ops. There's surprisingly little information out there on these topics. Even less for Typescript developers! I built a program and material that I wish I had when I started out: light on theory, heavy on practical application. Last week, our first cohort met for the last official session and nearly a dozen developers are now building some truly amazing products. This has been some of the most fun I've had teaching in a long time. Looking forward to cohort #2!	SHARE	Brian	Jenney	1,309	0	16	1	0	0	0.012987	#2	2026-02-04 09:25:48	https://www.linkedin.com/feed/update/urn:li:activity:7424865775259389952
urn:li:activity:7424503662942793728	Side Effects of Vibe Coding: • Sensitive API keys were embedded directly in client-side JavaScript bundles, making them publicly accessible. • Row Level Security (RLS) was disabled, allowing unauthenticated users full administrative read and write access to the database. • Approximately 1.5 million agent authentication tokens, 35,000 user email addresses, and over 4,𝟬𝟬𝟬 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 𝘄𝗲𝗿𝗲 𝗲𝘅𝗽𝗼𝘀𝗲𝗱. • Reported participation metrics were misleading — 𝟭.𝟱 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 “𝗮𝗴𝗲𝗻𝘁𝘀” 𝘄𝗲𝗿𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗲𝗱 𝗯𝘆 𝗷𝘂𝘀𝘁 𝟭𝟳,𝟬𝟬𝟬 𝗵𝘂𝗺𝗮𝗻 𝘂𝘀𝗲𝗿𝘀 (an 88:1 ratio). • Unauthenticated users could modify any live post on the platform, enabling widespread defacement and manipulation of content. • 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗱𝗶𝗿𝗲𝗰𝘁 𝗺𝗲𝘀𝘀𝗮𝗴𝗲𝘀 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝘁𝗵𝗶𝗿𝗱-𝗽𝗮𝗿𝘁𝘆 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗣𝗜 𝗸𝗲𝘆𝘀 stored in plaintext, putting users’ external accounts at risk. Maybe Moltbook is the warning we need. "𝘚𝘩𝘪𝘱 𝘧𝘢𝘴𝘵 𝘢𝘧 𝘶𝘴𝘪𝘯𝘨 𝘈𝘐 𝘵𝘰𝘰𝘭𝘴!" What could go wrong? A lot apparently.	IMAGE	Brian	Jenney	3,183	0	27	7	1	0	0.010996	null	2026-02-03 09:26:53	https://www.linkedin.com/feed/update/urn:li:activity:7424503662942793728
urn:li:activity:7424134545815560192	At Parsity, we’ve been against AI tool use while students are learning to code. This was not an easy decision and I'd be lying if I said I was 100% sure that this was the right move. Emerging research (and what we see in practice) supports our position: • 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘂𝗮𝗹 (𝗺𝗶𝘀)𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Students finish tasks without building real mental models • 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Fewer mistakes == fewer chances to learn how systems actually break • 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗰𝗼𝗱𝗲 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻: Reading AI output != knowing how to write or fix it yourself • 𝗦𝗸𝗶𝗹𝗹 𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲: Performance drops when the AI “exoskeleton” is removed Yes, AI can boost short-term productivity. But learning to code was never about speed. It’s about building the brain. That’s why our students learn fundamentals first. AI comes later. Once they can evaluate, debug, and supervise it. We’re not anti-AI. We’re pro-skill formation.	IMAGE	Brian	Jenney	1,330	0	20	7	0	0	0.020301	null	2026-02-02 09:00:09	https://www.linkedin.com/feed/update/urn:li:activity:7424134545815560192
urn:li:activity:7423838044660678656	While my YouTube channel has gained a lot of traction, my podcast is steadily declining in listeners. On the podcast, I feel like I've formed a deeper connection with listeners and can be a bit more nuanced in my advice. I'm making some changes to hopefully increase listeners on Spotify by going all in on video episodes. Tomorrow, the first video podcast drops and I'd love to hear from you if you're a listener of the show. What are topics you really want to know about? What are you struggling with? DM me or leave a comment and let me know if you want a shout out if I use your topic/question. THANK YOU FOR YOUR ATTENTION TO THIS MATTER 🫡	IMAGE	Brian	Jenney	1,780	0	16	6	0	0	0.01236	null	2026-02-01 13:21:58	https://www.linkedin.com/feed/update/urn:li:activity:7423838044660678656
urn:li:activity:7423413415185969152	The same company telling you "𝘞𝘦 𝘮𝘪𝘨𝘩𝘵 𝘣𝘦 6–12 𝘮𝘰𝘯𝘵𝘩𝘴 𝘢𝘸𝘢𝘺 𝘧𝘳𝘰𝘮 𝘮𝘰𝘥𝘦𝘭𝘴 𝘥𝘰𝘪𝘯𝘨 𝘮𝘰𝘴𝘵, 𝘮𝘢𝘺𝘣𝘦 𝘢𝘭𝘭, 𝘰𝘧 𝘸𝘩𝘢𝘵 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴 𝘥𝘰 𝘦𝘯𝘥-𝘵𝘰-𝘦𝘯𝘥" is also rapidly expanding. I wonder what roles they will be hiring for? 🤔	IMAGE	Brian	Jenney	34,873	0	79	18	2	0	0.002839	null	2026-01-31 09:14:38	https://www.linkedin.com/feed/update/urn:li:activity:7423413415185969152
urn:li:activity:7423056734421798913	I helped design the interview process for an AI start up where I used to work. The job listing was for an "AI Engineer". What that meant, practically, was: 1. 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗥𝗔𝗚: chunking && re-ranking and opinions on retrieval strategies 2. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀: tool-calling vs orchestration, evals and testing 3. 𝗔𝗴𝗲𝗻𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: LangGraph, Vercel's AI SDK, etc. and when it makes sense to use them... or not 4. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: how do you control token costs and see where agents fail or drift from their expected behavior? These skills really aren't too difficult for most experienced devs to learn. Yet, it's incredibly difficult to find people who already have practical experience, made the costly mistakes and know what to avoid. If you're a developer looking to build these skills for fun or to launch a product or maybe you're an engineering manager who is looking for a more technical understanding of RAG and agents, 𝗜 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗮 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗯𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴: https://lnkd.in/d6Pzfcg6	TEXT	Brian	Jenney	2,674	0	19	0	1	0	0.007479	null	2026-01-30 09:37:19	https://www.linkedin.com/feed/update/urn:li:activity:7423056734421798913
urn:li:activity:7423054982716891136	Code is just 1 tool in a developer’s box. There are some equally important ones you’ll want to pick up along the way: - project management - public speaking - system design Not only will these nice-to-haves become must-haves at some point in your career but they are more difficult to offload to an AI assistant.	TEXT	Brian	Jenney	509	0	5	0	1	0	0.011788	null	2026-01-30 09:30:21	https://www.linkedin.com/feed/update/urn:li:activity:7423054982716891136
urn:li:activity:7422688743532433409	Tech Layoffs Translated: “𝘞𝘦’𝘳𝘦 𝘧𝘭𝘢𝘵𝘵𝘦𝘯𝘪𝘯𝘨 𝘵𝘩𝘦 𝘰𝘳𝘨.” 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻: We overhired, and now we’re cutting back. “𝘞𝘦’𝘳𝘦 𝘣𝘦𝘤𝘰𝘮𝘪𝘯𝘨 𝘮𝘰𝘳𝘦 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵 𝘸𝘪𝘵𝘩 𝘈𝘐.” 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻: We told investors our massive bet on AI would pay off. It didn’t. Now we need to cut you. “𝘋𝘰𝘪𝘯𝘨 𝘮𝘰𝘳𝘦 𝘸𝘪𝘵𝘩 𝘭𝘦𝘴𝘴.” 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻: Tariffs, the markets, or simply bad bets have eaten our lunch. AI 𝘮𝘪𝘨𝘩𝘵 save us, but we don’t actually know how. We’ve heard some version of this nonsense since the pandemic. It likely won’t stop any time soon. Tech hiring continues to rise overall, but companies still over-hired, over-invested, and made short-sighted moves that are now being paid for by workers getting laid off. I was laid off a couple years ago. It was brutal. I had just bought a business, I have three kids and a mortgage. My biggest regret is not taking more time to reflect before jumping back into the job market. But I get it: you need a paycheck and the guise of stability. I did too. I ended up becoming part of the great reshuffle: moving from management and web dev into working with LLMs, AI and product engineering. If you’re impacted by layoffs, I’m sorry and I can relate. But if there’s any silver lining, it’s this: the next era of tech is being built 𝘳𝘪𝘨𝘩𝘵 𝘯𝘰𝘸. And the skills you build next could shape your entire career. Good luck out there. I made a project for full stack developers who want to learn AI practically: https://lnkd.in/d6Pzfcg6	ARTICLE	Brian	Jenney	7,080	0	36	3	1	0	0.00565	null	2026-01-29 09:15:03	https://www.linkedin.com/feed/update/urn:li:activity:7422688743532433409
urn:li:activity:7422676002063110144	Junior developers are inherently risky (in every industry, not just tech btw). 𝘉𝘶𝘵... 𝘐 𝘢𝘮 𝘢 𝘫𝘶𝘯𝘪𝘰𝘳, you say. Yeah, I get it, I get it, just bare with me here. The word junior is subjective. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗯𝗮𝘀𝗶𝗰𝗮𝗹𝗹𝘆 𝗯𝗼𝗶𝗹𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 𝗶𝘀 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝘄𝗵𝗼 𝗵𝗮𝘀 𝘀𝗼 𝗹𝗶𝘁𝘁𝗹𝗲 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝘁𝗿𝘂𝘀𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼 𝗺𝘂𝗰𝗵 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻. • They need significant hand-holding for months on the job. • They will take on the easy stuff and maybe break some things. • They won't contribute much. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗴: nearly every developer will be junior when they first join a company unless they are very senior. You don't actually need to a be a mid level developer to be a safe hire. You just need to avoid the tell-tale signs that you are, in fact, a n00b. Quite simply, stop down-playing yourself. • 𝗥𝗲𝗺𝗼𝘃𝗲 𝗷𝘂𝗻𝗶𝗼𝗿, 𝗮𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 from your resume and LinkedIn. • DON'T talk about the project from your school or bootcamp • 𝗗𝗢 𝗧𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗮𝗽𝗽 𝗼𝗿 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝘆𝗼𝘂'𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 - yes, you need to have a project to talk about. • 𝗨𝘀𝗲 𝘀𝘁𝗿𝗼𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝘁𝗼 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 and speak to the benefits of features you created. • Instead of created a full stack app with a laundry list of tech try migrated a JS app to Typescript to improve developer experience and velocity. I break down why you need to be building a product instead of a portfolio in 2026 with some resources to help you get started: https://lnkd.in/gNuYpYfR	ARTICLE	Brian	Jenney	2,063	0	20	3	0	0	0.011149	null	2026-01-29 08:24:25	https://www.linkedin.com/feed/update/urn:li:activity:7422676002063110144
urn:li:activity:7421979306819772416	Apparently, if you’re not using: • Opus 4.5 (but only for certain tasks) • A different model for planning vs coding vs refactoring • Custom rule files • Per-project AI configs • MD spec / rules docs • Spec-driven planning phases • Git subtrees • “Ralph loops” • Spec kit • Carefully segmented prompting workflows …then you’re "not using AI correctly." To be fair, some of this is spot on. 𝗕𝘂𝘁 𝗮𝘁 𝘀𝗼𝗺𝗲 𝗽𝗼𝗶𝗻𝘁 𝘄𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝗽𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗮𝘀𝗸: If it takes this much infrastructure to get AI to help us write decent code… 𝗜𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗿𝗲𝗮𝗹𝗹𝘆 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿? Or is the tooling still not as intuitive as we pretend it is? The biggest performance gains I’ve seen don’t come from elaborate toolchains. They come from: • Understanding the problem • Being able to describe the problem clearly • Giving the model the right context (look at 𝚊𝚐𝚎𝚗𝚝𝚜.𝚝𝚜 as an example to scaffold the next agent) • Reviewing and iterating Yes, rules files help. Yes, better workflows help. Yes, model choice matters. But if someone needs a 14-step AI ritual just to build a CRUD app, we may have crossed from "power tools" into magic spells. 𝗔𝗿𝗲 𝘄𝗲 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗮𝗱𝗱𝗶𝗻𝗴 𝗮 𝘀𝗲𝗰𝗼𝗻𝗱 𝗷𝗼𝗯 𝗮𝘀 𝗮𝗻 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿? I'm not saying either is wrong... I'm just saying.	TEXT	Brian	Jenney	17,788	0	88	42	5	0	0.007589	null	2026-01-27 10:16:00	https://www.linkedin.com/feed/update/urn:li:activity:7421979306819772416
urn:li:activity:7421597826318618625	"𝘞𝘢𝘳 𝘪𝘴 𝘱𝘦𝘢𝘤𝘦. 𝘍𝘳𝘦𝘦𝘥𝘰𝘮 𝘪𝘴 𝘴𝘭𝘢𝘷𝘦𝘳𝘺. 𝘐𝘨𝘯𝘰𝘳𝘢𝘯𝘤𝘦 𝘪𝘴 𝘴𝘵𝘳𝘦𝘯𝘨𝘵𝘩." I can only dissociate through code and AI hot takes for so long. It feels like a dark time is upon us in the US. But I'm always optimistic. You may not know me personally, but I hope you're doing alright and keeping sane in an increasingly chaotic world. RIP Alex Pretti	TEXT	Brian	Jenney	1,237	0	29	0	1	0	0.024252	null	2026-01-26 09:00:08	https://www.linkedin.com/feed/update/urn:li:activity:7421597826318618625
urn:li:activity:7420542285995712512	This guy said it better than I can.	SHARE	Brian	Jenney	1,604	0	15	4	0	0	0.011845	null	2026-01-23 11:05:47	https://www.linkedin.com/feed/update/urn:li:activity:7420542285995712512
urn:li:activity:7420152017597366274	If you’re using AI to write your code, here’s something you should know. If you are: • using AI to generate production code • using AI to generate the tests • using AI to validate its own output • and then shipping it Then you are MORE likely than a human to ship bugs and vulnerabilities. Specifically, you are more likely than a human to: • Introduce security vulnerabilities (~20-25% higher likelihood) • Ship critical or major bugs (≈1.5–1.7× more per PR) • Miss edge cases and failure paths in tests • Over-test happy paths and under-test bad inputs • Have “clean” builds that hide production risks AI-written tests are less likely to test edge cases and over-use mocks. LGTM! 😅 This doesn’t mean “don’t use AI.” It means 100% AI-generated systems should make you pause and consider the risks. Output scales fast. Judgment and maintenance won't.	TEXT	Brian	Jenney	5,108	0	28	10	2	0	0.007831	null	2026-01-22 09:15:00	https://www.linkedin.com/feed/update/urn:li:activity:7420152017597366274
urn:li:activity:7419455185086914560	If you're a Typescript developer and you want to get started with AI agents, just do this: 1. Put $5 into OpenAI's API platform (or use Gemini's free tier) https://lnkd.in/gPM8D2Pq 2. Install Vercel's AI SDK: https://lnkd.in/gh-sND6r 3. Read up on common patterns for agents (human in the loop, ReAct, orchestration): https://lnkd.in/g3ekb3xr 4. Create a simple agent using tool-calling to do an internet search and summarize the results. You can use this API: https://www.searchapi.io/ 5. Re-build your agent with a popular framework like LangGraph: https://lnkd.in/g9yB-_ec 6. Add unit tests and/or evals for your agent 7. Add an observability layer with Helicone or Langsmith: https://www.helicone.ai/ 8. Grab this project to build your first agent: https://lnkd.in/gMv3x3tS 𝗦𝘁𝗲𝗽 𝟴.𝟱 - spend less time online arguing about whether or not LLMs will replace developers with people who can't solve FizzBuzz.	TEXT	Brian	Jenney	2,454	0	33	1	0	0	0.013855	null	2026-01-20 11:06:02	https://www.linkedin.com/feed/update/urn:li:activity:7419455185086914560
urn:li:activity:7419046027644678144	Nearly 100% of my code is AI-generated. That doesn’t mean I’m not building software - it just means I’m not the one writing the code. It's a weird time to be a coder but there's also massive opportunity hiding behind the hype and fear mongering...	ARTICLE	Brian	Jenney	4,972	0	33	10	0	0	0.008648	null	2026-01-19 08:00:12	https://www.linkedin.com/feed/update/urn:li:activity:7419046027644678144
urn:li:activity:7417986432369078272	Wonder if Claude can fix the terminal flicker next 😅	SHARE	Brian	Jenney	3,854	0	8	0	0	0	0.002076	null	2026-01-16 09:49:44	https://www.linkedin.com/feed/update/urn:li:activity:7417986432369078272
urn:li:activity:7417349877958914048	Do not attend if: 1. You're studying for Google, Meta, Netflix or massive tech orgs 2. You want some magical prompts to "hack" the interview 3. You'd just use a cache 😉 We're going to cover 3.5 patterns to help you nail your next technical interview outside big tech.	EVENT	Brian	Jenney	4,318	0	34	3	0	0	0.008569	null	2026-01-14 15:40:18	https://www.linkedin.com/feed/update/urn:li:activity:7417349877958914048
urn:li:activity:7417264206913200128	We get it. AI wRiTeS thE COde! While most devs are optimizing their prompts, I think the fun (and opportunity) is a couple layers below: 1. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 using frameworks like LangChain, Vercel's AI SDK or rolling your own 2. Learning how to to 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 𝘀𝗲𝗮𝗿𝗰𝗵 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗶𝗻 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 like Qdrant and Pinecone 3. 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗲𝗿𝗮 using tools like Firecrawl and 𝚌𝚛𝚊𝚠𝚕𝟺𝚊𝚒 4. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - basically figuring out how to organize what an LLM needs to know at what stage to give you a good response 5. 𝗟𝗟𝗠-𝗼𝗽𝘀 𝗮𝗻𝗱 𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Monitoring token costs and model drift to see what breaks and why. While everyone is glazing Claude or arguing over replacement theories, you could build up these skills and be prepared for software 3.0	TEXT	Brian	Jenney	3,103	0	27	5	0	0	0.010313	null	2026-01-14 09:59:53	https://www.linkedin.com/feed/update/urn:li:activity:7417264206913200128
urn:li:activity:7416910212789542912	Challenge accepted. Last year I spent a lot of time rallying against AI hype. I also said a lot of things that are now less true than they were 6 months ago. The models have improved. Our collective workflows have improved. I'm honestly not sure where the limit is and I'm excited John Crickett is working to find it.	SHARE	Brian	Jenney	1,940	0	21	1	0	0	0.01134	null	2026-01-13 10:33:14	https://www.linkedin.com/feed/update/urn:li:activity:7416910212789542912
urn:li:activity:7416886820073869312	2 things: 1. Are CEOs of large companies really begging their eng department for... anything? Let alone to build a prototype? Why? 2. If you ARE doing this... cool! Using app-gen tools like v0, Lovable or Replit are great ways to create a "cheap" proof of concept with basic functionality. Oh and lastly, if you're one of those dudes who claims they built an app over the weekend that is now doing 10K MRR and you have no clue how it works... you can take a long walk off a short bridge.	IMAGE	Brian	Jenney	25,578	0	48	16	2	0	0.00258	null	2026-01-13 09:00:16	https://www.linkedin.com/feed/update/urn:li:activity:7416886820073869312
urn:li:activity:7416520611809640448	White board interviews are biased, archaic and don't reflect the kind of work we actually do as developers. Ok. Let's pretend you're correct. What do you do now? You can either: a. Avoid DSA and all companies that ask these questions (𝘐 𝘵𝘳𝘪𝘦𝘥 𝘵𝘩𝘪𝘴 𝘧𝘰𝘳 𝘺𝘦𝘢𝘳𝘴 𝘸𝘪𝘵𝘩 𝘷𝘢𝘳𝘺𝘪𝘯𝘨 𝘳𝘦𝘴𝘶𝘭𝘵𝘴) b: Learn some of the most common patterns and data structures that make up the majority of questions out there You can still avoid whiteboard interviews, because you WANT to, not because you HAVE to. PS 👇 I break down 3 patterns you WILL encounter on the interview circuit and a free guide for developers who want to learn through hands-on challenges: https://lnkd.in/gEYY9sF5	TEXT	Brian	Jenney	1,024	0	8	1	0	0	0.008789	null	2026-01-12 08:45:06	https://www.linkedin.com/feed/update/urn:li:activity:7416520611809640448
urn:li:activity:7415784721013506048	During a live coding interview last year, I instinctively switched from Cursor to VS Code. The CTO stopped me. “No — leave Cursor on. We want to see how you solve this with AI.” I thought the interview would be easier. It wasn’t.	ARTICLE	Brian	Jenney	9,743	0	46	5	1	0	0.005337	null	2026-01-10 08:00:56	https://www.linkedin.com/feed/update/urn:li:activity:7415784721013506048
urn:li:activity:7415238834323636224	So you can just write Python in your NextJS app now. What will those maniacs at Vercel cook up next? 🙇‍♂️	IMAGE	Brian	Jenney	2,598	0	31	10	0	0	0.015781	null	2026-01-08 19:51:46	https://www.linkedin.com/feed/update/urn:li:activity:7415238834323636224
urn:li:activity:7414349222793117696	I'm genuinely excited to walkthrough any of these topics. If there's something else you'd like to see, let us know in the comments or DM me.	SHARE	Brian	Jenney	1,411	0	8	0	1	0	0.006378	null	2026-01-06 08:56:46	https://www.linkedin.com/feed/update/urn:li:activity:7414349222793117696
urn:li:activity:7413993429594103808	My absolute worst interview experiences: 1. That time the guy at Google told me "𝘵𝘩𝘪𝘴 𝘪𝘴 𝘸𝘩𝘺 𝘸𝘦 𝘥𝘰𝘯'𝘵 𝘩𝘪𝘳𝘦 𝘴𝘦𝘭𝘧-𝘵𝘢𝘶𝘨𝘩𝘵 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴" when I struggled to implement a BST. 2. When I ended the Meta phone screen in the first 15 minutes after I was asked a problem that involved recursion. 3. The interview that turned into a lesson on objects in JS when I thought {} === {} 🤦 (𝘴𝘵𝘪𝘭𝘭 𝘦𝘮𝘣𝘢𝘳𝘳𝘢𝘴𝘴𝘦𝘥 𝘣𝘺 𝘵𝘩𝘪𝘴 𝘰𝘯𝘦) 4. Had a literal panic attack during the last stage for a role I wanted. It was completely non-technical too. I asked if we could start over... 😅 Listen, I've sucked at interviews and through lots of trial, error, practice and more practice, I forced myself to suck less. I break down the exact things I did in this video and give away my repo full of coding challenges (𝘣𝘶𝘵 𝘰𝘯𝘭𝘺 𝘧𝘰𝘳 𝘢𝘣𝘰𝘷𝘦 𝘢𝘷𝘦𝘳𝘢𝘨𝘦 𝘭𝘰𝘰𝘬𝘪𝘯𝘨 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘦𝘳𝘴): https://lnkd.in/gt4Uzrmm	TEXT	Brian	Jenney	135,072	0	203	49	0	0	0.001866	null	2026-01-05 09:22:58	https://www.linkedin.com/feed/update/urn:li:activity:7413993429594103808
urn:li:activity:7413981477610303491	My mom kicked off my coding career with an intervention. Over a decade ago, my mom walked into my girlfriend's condo and handed me a note with an ultimatum: Get sober or get out of your kids lives. I was a mess. I owed people money. I was getting threats on my phone. I almost lost my life in a robbery gone wrong. My friend had taken his life and his brother got sentenced to state time. It was only a matter of time before I lost my life or my freedom. Didn't matter. I wasn't ready to quit yet. I told her I'd try just so she'd stop crying. One day turned to a few days. Then a week went by. Then a month. I either couldn't sleep or would sleep for 12 hours. I ate too much candy. I lost "friends." I got better. Coding became my new addiction. It didn't make any sense - I had no technical background and didn't own a computer for most of my life but I loved solving problems with code. This new addiction led to a new career and my habits snowballed. I lost weight, stopped smoking and picked up reading. I became a better father. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗼 𝗰𝗼𝗱𝗲 𝗱𝗶𝗱 𝗡𝗢𝗧 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝘆 𝗹𝗶𝗳𝗲. Getting sober did. Coding certainly changed my wallet however. Sometimes I reveal this embarrassing aspect of my life because I know how it feels to feel like you're alone or like the odds are impossibly stacked against you. If you're going through something similar I hope you know it's not impossible and the world can really open up once you get of your own way. I break down some practical steps to building habits that stick here and hopefully you find it helpful: https://lnkd.in/gGzwfbSW	ARTICLE	Brian	Jenney	1,792	0	27	4	1	0	0.017857	null	2026-01-05 08:35:29	https://www.linkedin.com/feed/update/urn:li:activity:7413981477610303491
urn:li:activity:7412920350268846080	If learning JavaScript feels like you’re starting over every few months, just stop. The issue is the way you’re learning. Here’s the 5-step system I’d use in 2026: 1. 𝗞𝗶𝗹𝗹 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹 𝗮𝗱𝗱𝗶𝗰𝘁𝗶𝗼𝗻: Watch just enough to start, then pause. Rewrite, extend, or break the example before you hit play again. 2. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Variables, functions, arrays/objects, this/binding, async/await. Litmus test: build a vanilla JS form with validation that posts to an API. 3. 𝗨𝘀𝗲 𝗯𝘂𝘁𝘁𝗼𝗻-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Add a button, decide what should happen, then make it real. Ship tiny experiments in CodePen/StackBlitz. 4. 𝗚𝗲𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Mentor > meetups > paid review > post your code. Pair program with someone slightly ahead of you. 5. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺: 10–30 minutes daily beats 8 hours on Saturday. Plan tomorrow’s task tonight. Keep the habit alive. If you want structure, accountability, and feedback to actually stick the fundamentals, start with Dev30 (30 days, JS fundamentals with office hours): https://dev30.xyz I break down how I'd be learning JS in 2026 if I was starting over here: https://lnkd.in/gkPW5nUP	TEXT	Brian	Jenney	6,625	0	40	4	1	0	0.006792	null	2026-01-02 10:18:56	https://www.linkedin.com/feed/update/urn:li:activity:7412920350268846080
urn:li:activity:7412546240384794625	The 2026 goal for Parsity is simple: More student success. This is a small subset of congratulatory posts I found in Slack. I've said "𝙘𝙤𝙣𝙜𝙧𝙖𝙩𝙪𝙡𝙖𝙩𝙞𝙤𝙣𝙨!" to more people than I can remember over the years. It never gets old. This market is not easy for career changers. It's also not impossible. There is no silver bullet either. Some were hired before completing our program. Others took months to land a role. Some students did internships. Some built in public. Some mass applied. You have enough doom and gloom in your feed. Whether or not you ever consider joining Parsity (https://parsity.io) - I hope this gives you a little boost heading into the new year.	IMAGE	Brian	Jenney	1,824	0	56	7	0	0	0.034539	null	2026-01-01 09:32:22	https://www.linkedin.com/feed/update/urn:li:activity:7412546240384794625
urn:li:activity:7411805138450935808	Here are 4 mistakes I made when building agents: ❌ 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗜 𝗻𝗲𝗲𝗱𝗲𝗱 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗽𝗹𝗮𝗰𝗲 ✅ If the steps are deterministic and predictable, 𝗮 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝘁𝗵𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗼𝗽𝘁𝗶𝗼𝗻. Agents make sense when planning, exploration, or autonomy are required. ❌ 𝗣𝗮𝘀𝘀𝗶𝗻𝗴 𝘁𝗼𝗼 𝗺𝘂𝗰𝗵 (𝗼𝗿 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴) 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 ✅ Good agents get the right information at the right moment. Context should be intentional and managed, not a full memory dump. ❌ 𝗣𝗿𝗼𝗺𝗽𝘁 (𝗼𝘃𝗲𝗿)𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 ✅ Clear structure, consistent formatting, and a few concrete examples outperform clever wording. ❌ 𝗨𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆 𝘁𝗮𝘀𝗸 ✅ Different tasks benefit from different models. Routing, extraction, and classification don’t need heavyweight reasoning. I break down more common pitfalls (and how I avoid them now) in this video 👇 https://lnkd.in/gv-Nqjuz	ARTICLE	Brian	Jenney	1,768	0	8	1	0	0	0.00509	null	2025-12-30 08:27:29	https://www.linkedin.com/feed/update/urn:li:activity:7411805138450935808
urn:li:activity:7411483215850508288	Typical advice to junior developers on Reddit: The market is cooked. No one is hiring juniors. AI has replaced you. Just give up. Truly, thought provoking stuff. Let's go past the headlines and edge-lord takes and learn what you can practically do to create a larger surface area for luck going into 2026.	ARTICLE	Brian	Jenney	3,748	0	30	16	2	0	0.012807	null	2025-12-29 11:08:17	https://www.linkedin.com/feed/update/urn:li:activity:7411483215850508288
urn:li:activity:7411462800864272384	2025 was the year of layoffs, hype cycles, and broken promises. Some trends clearly moved the industry forward and others did real damage. Here’s the best and absolute worst tech trends coming out of 2025 (IMO). ❌ 𝗪𝗼𝗿𝘀𝘁 𝗧𝗿𝗲𝗻𝗱𝘀 𝟭. 𝗕𝗼𝗼𝘁𝗰𝗮𝗺𝗽𝘀 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗶𝗻𝗴 Sentiment about coding is at an all time low. So many bootcamps closed. 𝗧𝗵𝗲 𝗰𝘆𝗻𝗶𝗰𝘀 𝗮𝗿𝗲 𝗵𝗮𝗽𝗽𝘆 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗶𝘀. It does leave a massive gap for adults who want to change careers. This makes me sad. 𝟮. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝗵𝘆𝗽𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 We were told output would explode. Instead, we got more bugs, more rewrites, and more pressure to ship faster without better systems in place. 𝟯. 𝗝𝘂𝗻𝗶𝗼𝗿𝘀 𝗴𝗼𝘁 𝘀𝗾𝘂𝗲𝗲𝘇𝗲𝗱 “Entry-level” now means “ready on day one.” ✅ 𝗕𝗲𝘀𝘁 𝗧𝗿𝗲𝗻𝗱𝘀 𝟭. 𝗕𝗼𝗼𝘁𝗰𝗮𝗺𝗽𝘀 𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗶𝗻𝗴 (𝘆𝘂𝗽, 𝗼𝗻 𝗯𝗼𝘁𝗵 𝗹𝗶𝘀𝘁𝘀) 3 months to 100K is dead. This creates room for honest education: no timelines, no guarantees, no pretending this is easy. That’s a win long-term IMO. 𝟮. 𝗦𝗲𝗻𝗶𝗼𝗿 𝗮𝗻𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝘁 𝗱𝗲𝗺𝗮𝗻𝗱 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝘀𝘂𝗿𝗴𝗲𝗱 Smaller, leaner teams meant more demand for people who are full stack and senior. 𝟯. 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗴𝗼𝘁 𝗴𝗲𝗻𝘂𝗶𝗻𝗲𝗹𝘆 𝘂𝘀𝗲𝗳𝘂𝗹 Claude and Cursor went from fun little tools to core infrastructure with crazy high retention among developers. Agents are moving past the hype cycle into boring, practical use cases. 𝟰. 𝗛𝗶𝗿𝗶𝗻𝗴 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘇𝗲𝗱 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 “𝗯𝗼𝗼𝗺𝗶𝗻𝗴” 2025 will end with more open roles than it began with. The market is lop-sided but trending in a positive direction. 𝗛𝗼𝘄 𝘁𝗼 𝘁𝗮𝗸𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗴𝗼𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝟮𝟬𝟮𝟲: 1. Stop chasing shortcuts and build projects over portfolios 2. Learn how systems work by reading up on system design 3. Treat education like a long-term investment, not a lottery ticket 4. If you're front-end-only, expand your skillset towards backend Curious what trends you think I missed. Or got wrong. https://lnkd.in/gc3S2Rhm	ARTICLE	Brian	Jenney	1,350	0	18	1	0	0	0.014074	null	2025-12-29 09:47:10	https://www.linkedin.com/feed/update/urn:li:activity:7411462800864272384
urn:li:activity:7410391300547739649	"Maybe you should just quit." I honestly didn't expect Michael Greenspan to be so direct during a meeting we had a few months back. I was unhappy, stressed and burnt out. Michael saw that and led me to the same conclusion I was trying to avoid. We talked through the worst case scenario which honestly wasn't so bad. My kids wouldn't go hungry or lose the roof over their head. This wasn't life or death. It just felt like it. I made a plan and handed in my 2 weeks notice. Now what? • I spent the first 2 weeks on Parsity's new AI curriculum and launched our first cohort which is nearly sold out. This was so much fun to create. • Renewed focus on YouTube to attract students and pumped out more videos. Nothing, nothing, nothing then 1 video hit well over 100K views. So many trolls. Way more love. • Accepted an offer with a company and team I'm excited to work with. I swear the lord works in mysterious ways. • That massive tension headache I'd gotten used to disappeared. Thanks Michael Greenspan for giving me room to vent and pushing me to do the uncomfortable.	TEXT	Brian	Jenney	6,438	0	81	5	0	0	0.013358	null	2025-12-26 10:49:24	https://www.linkedin.com/feed/update/urn:li:activity:7410391300547739649
urn:li:activity:7409675055577763842	AI didn’t fail this year. It failed expectations. No mass job extinction. No solo unicorn founders. No “prompt your way to riches.” Reality was less marketable: • uneven usefulness • uneven adoption • a widening gap between people who know how to think and people hoping AI will think for them Developers felt this first. AI raised expectations. Output increased. So did bugs. We all felt a historic number of outages. 𝗠𝘆 𝟮𝟬𝟮𝟲 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 1. 𝘈𝘐 𝘴𝘦𝘵𝘵𝘭𝘦𝘴 𝘪𝘯𝘵𝘰 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦. Agents improve in reliability and scope. Teams stop chasing novelty and start standardizing workflows. Excitement fades. Usefulness increases. Agent design patterns solidify the same way frameworks and cloud architectures eventually did. 2. 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘤𝘩𝘢𝘯𝘨𝘦𝘴 𝘴𝘩𝘢𝘱𝘦 Learners reach for AI as a teacher first. Tutorials lose relevance because the fastest path to understanding becomes interactive and contextual. I also don't believe this will work. Hallucinations and overly general learning paths frustrate users moving past the beginner stage. People rediscover mentorship and opt for more human experiences. 3. 𝘛𝘦𝘤𝘩 𝘪𝘯𝘵𝘦𝘳𝘷𝘪𝘦𝘸𝘴 𝘨𝘦𝘵... 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 Interviews evolve around AI fluency. Candidates are evaluated on how they reason with tools, how they validate output, and how they recover when AI is confidently wrong. The skill becomes collaboration && delegation. 4. 𝘈𝘐 𝘱𝘳𝘦𝘴𝘴𝘶𝘳𝘦 𝘴𝘱𝘳𝘦𝘢𝘥𝘴 𝘵𝘰 𝘰𝘧𝘧𝘪𝘤𝘦 𝘸𝘰𝘳𝘬𝘦𝘳𝘴 Office and knowledge workers begin to feel the same pressure developers felt earlier to "move faster" They’re expected to produce more with AI but given little guidance on how to structure work, evaluate output, or integrate tools into real processes. That gap creates space for a new role: people who translate ambiguous business needs into structured workflows, systems, and AI-assisted processes. Not traditional developers but systems-oriented operators embedded in non-technical teams. How off base am I?	TEXT	Brian	Jenney	9,953	0	56	21	1	0	0.007837	null	2025-12-24 11:23:18	https://www.linkedin.com/feed/update/urn:li:activity:7409675055577763842
urn:li:activity:7409273301421744129	LGTM 👌 This won't be a shocker to developers who use AI coding tools everyday but it makes you wonder: 𝘐𝘴 𝘵𝘩𝘦 𝘫𝘶𝘪𝘤𝘦 𝘸𝘰𝘳𝘵𝘩 𝘵𝘩𝘦 𝘴𝘲𝘶𝘦𝘦𝘻𝘦? 𝘐𝘴 𝘪𝘵 𝘱𝘶𝘳𝘦 𝘤𝘰𝘪𝘯𝘤𝘪𝘥𝘦𝘯𝘤𝘦 𝘵𝘩𝘢𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘮𝘢𝘫𝘰𝘳 𝘰𝘶𝘵𝘢𝘨𝘦𝘴 𝘩𝘢𝘷𝘦 𝘰𝘤𝘤𝘶𝘳𝘳𝘦𝘥 𝘵𝘩𝘪𝘴 𝘺𝘦𝘢𝘳 𝘢𝘴 𝘸𝘦 𝘳𝘦𝘭𝘺 𝘰𝘯 𝘰𝘶𝘳 𝘈𝘐 "𝘤𝘰-𝘸𝘰𝘳𝘬𝘦𝘳𝘴"? The tools are here to stay. I'm not cancelling my Claude or Cursor subscriptions but reading CodeRabbit's report validates what many of us have experienced. AI coding tools can be amazing BUT: • They introduce 𝗺𝗼𝗿𝗲 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 & 𝗺𝗮𝗷𝗼𝗿 𝗶𝘀𝘀𝘂𝗲𝘀 • 𝗘𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗴𝗮𝗽𝘀 show up far more often • Performance bugs are rarer, but 𝗱𝗶𝘀𝗽𝗿𝗼𝗽𝗼𝗿𝘁𝗶𝗼𝗻𝗮𝘁𝗲𝗹𝘆 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 • 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 scale faster than human ones • AI PRs often 𝗰𝗼𝘀𝘁 𝗺𝗼𝗿𝗲 𝗿𝗲𝘃𝗶𝗲𝘄 𝘁𝗶𝗺𝗲, not less This isn't some "dunk" on AI-generated code. It's better the devil you know than the devil you don't. LGTM!	IMAGE	Brian	Jenney	3,093	0	27	5	2	0	0.010993	null	2025-12-23 08:46:52	https://www.linkedin.com/feed/update/urn:li:activity:7409273301421744129
urn:li:activity:7408902577347653632	5 things that nearly derailed my developer career: 1. 𝗡𝗼𝘁 𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗻𝗴. I would've job hopped a lot less if I had been up front with what I wanted. 2. 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 "𝗽𝗹𝗮𝘆𝗶𝗻𝗴 𝗶𝘁 𝘀𝗮𝗳𝗲" 𝘄𝗮𝘀 𝘀𝗮𝗳𝗲. No opinion, no questions... no visibility. 3. 𝗔𝘃𝗼𝗶𝗱𝗶𝗻𝗴 𝗗𝗦𝗔. Seriously, it's not that hard to learn and gave me more tools to solve coding problems. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗼𝘃𝗲𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀. It's really tough to debug or move between frameworks when you don't have a solid foundation. 5. 𝗥𝘂𝗯𝗯𝗲𝗿-𝘀𝘁𝗮𝗺𝗽𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝗿𝗲𝘃𝗶𝗲𝘄𝘀. This is embarrassing - I let too many issues slide by because I thought I had better things to do 😬 If I was starting off today, I'm sure I'd be over-relying on AI tools instead of StackOverflow and following the advice of every bald headed influencer online who I think has it figured it out. Hindsight is 20/20 and it's one of the reasons I write online in the hopes maybe you'll avoid some of these pitfalls (and maybe even join me at Parsity: https://parsity.io). If you've written code for a while, what are some mistakes you wish you avoided when you first started?	TEXT	Brian	Jenney	7,907	0	40	10	0	0	0.006324	null	2025-12-22 08:13:45	https://www.linkedin.com/feed/update/urn:li:activity:7408902577347653632
urn:li:activity:7407834608500789248	"𝘞𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 𝘣𝘦 𝘢𝘣𝘭𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘵𝘸𝘪𝘤𝘦 𝘢𝘴 𝘧𝘢𝘴𝘵 𝘸𝘪𝘵𝘩 𝘩𝘢𝘭𝘧 𝘵𝘩𝘦 𝘱𝘦𝘰𝘱𝘭𝘦." That was the promise of AI. It's failing a lot of developers. Leaders believe the hype. Teams get thrown into AI workflows with no training. And we're all supposed to "figure it out" from viral posts. 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗺𝗲 (𝗳𝗼𝗿 𝗻𝗼𝘄 𝗮𝘁 𝗹𝗲𝗮𝘀𝘁): 1. 𝗣𝗹𝗮𝗻 𝗳𝗶𝗿𝘀𝘁: I voice out the idea to ChatGPT to clarify direction and confirm my understanding. 2. 𝗙𝗶𝗻𝗱 𝗮 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Either existing examples or one I write myself to feed my robot. 3. 𝗦𝘁𝗮𝗿𝘁 𝘁𝗶𝗻𝘆: Never trust AI with big refactors upfront. 4. 𝗗𝗲𝗾𝘂𝗲𝘂𝗲 𝘁𝗮𝘀𝗸𝘀: Once a pattern works, I feed the tool more tasks. 5. 𝗥𝗲𝘃𝗶𝗲𝘄 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆: Draft PR, check everything, then commit. 𝘞𝘩𝘦𝘯 𝘐'𝘮 𝘦𝘹𝘱𝘦𝘳𝘪𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘰𝘳 𝘴𝘵𝘳𝘦𝘴𝘴-𝘵𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘯 𝘪𝘥𝘦𝘢? I ignore the rules and let AI run wild just to see if the approach isn't a waste. Small experiments are cheaper to try with AI. I break down some practical ways I do and absolutely do NOT use AI in this article: https://lnkd.in/gfHut-7q	ARTICLE	Brian	Jenney	1,935	0	16	8	0	0	0.012403	null	2025-12-19 09:30:01	https://www.linkedin.com/feed/update/urn:li:activity:7407834608500789248
urn:li:activity:7407468096439037952	My most popular video on YT isn't controversial at all. I just said what most developers already know: AI tools are great. They also: • Often slow us down • Create unrealistic expectations around productivity • Allow non-coders to create a mess at breakneck speed • Aren't the great replacement that the media keeps shoving down our throat Look - I pay for the tools. I use them literally every day. I've spent a solid portion of this year sharing my experience with Claude, Cursor and Gippity and where they fall short. Looks like I'm not alone.	ARTICLE	Brian	Jenney	4,149	0	40	24	2	0	0.015907	null	2025-12-18 09:13:38	https://www.linkedin.com/feed/update/urn:li:activity:7407468096439037952
urn:li:activity:7407102288575647744	Web devs: here’s a practical path to learn AI without quitting your day job: What you’ll be able to build in ~6–8 weeks (1–2 hrs/night): a docs Q&A bot for your repo, a customer-support search tool, or a summarizer that plugs into your app. Real projects, not demo toys. 𝗧𝗵𝗲 𝗽𝗮𝘁𝗵: • 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 𝗿𝗶𝗴𝗵𝘁: tokens, embeddings, vectors, context window, latency/cost. You don’t need PhD math - just enough to reason about tradeoffs. • 𝗦𝗵𝗶𝗽 𝗮 𝘁𝗶𝗻𝘆 𝘀𝘁𝗮𝗿𝘁𝗲𝗿: Next.js + Vercel AI SDK (or OpenAI/Anthropic SDK). One route, one prompt, stream the response. Push to prod day 1. • 𝗔𝗱𝗱 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗥𝗔𝗚): chunk your own docs/README/issues, embed them, store in pgvector/Supabase or Pinecone, then "ground" answers with your data. • 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘂𝘀𝗮𝗴𝗲/𝗰𝗼𝘀𝘁𝘀: log tokens and latency per request. Set monthly caps. • 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝘁𝗵𝗲 𝗨𝗫: add tool/function calling for structured outputs, retries with fallbacks, and guardrails for edge cases. • 𝗦𝗵𝗶𝗽 𝗮 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲: deploy, add analytics, and get someone (even just you at work) using it for a week. Iterate on what breaks. 𝗪𝗵𝗮𝘁 𝘁𝗼 𝘀𝗸𝗶𝗽 (𝗳𝗼𝗿 𝗻𝗼𝘄): • Building a ChatGPT clone • Rewriting your stack around the “framework of the week” • Deep-diving transformer math before you’ve shipped anything • Chasing the newest model every 48 hours 𝗔 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲: • Week 1: Starter app + streaming + basic prompt • Weeks 2–3: RAG with your data + evals • Weeks 4–5: Monitoring, cost controls, tool calls • Weeks 6–8: Ship a real workflow and iterate 𝗪𝗮𝗻𝘁 𝗮 𝗻𝗼‑𝗕𝗦 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁? • AI with JavaScript (hands-on guide): https://lnkd.in/gPWqvEff • AI with RAG (project guide): https://lnkd.in/d6Pzfcg6 If you can build a CRUD app, you can build with AI. The trick is focusing on data and shipping small (but useful) tools.	TEXT	Brian	Jenney	3,166	0	23	11	0	0	0.010739	null	2025-12-17 09:00:02	https://www.linkedin.com/feed/update/urn:li:activity:7407102288575647744
urn:li:activity:7406740305170948096	AI wrote this. I’m testing a new app I built that can draft posts in my voice, suggest relevant lead magnets, and pull in examples from past content. It uses tool-calling, RAG, and a vector index of 1,000+ of my posts to get close to how I actually write. Why share this? Because “AI for content” doesn’t have to be fluff. Here’s a practical example and how I got it working. 𝗪𝗵𝗮𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀: • 𝗜𝗺𝗶𝘁𝗮𝘁𝗲𝘀 𝗺𝘆 𝘃𝗼𝗶𝗰𝗲: It retrieves similar posts from my archive and mirrors tone, length, and structure. • 𝗞𝗻𝗼𝘄𝘀 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲: It can suggest relevant resources (like an AI lead magnet) and drop the right links. • 𝗦𝘁𝗮𝘆𝘀 𝗼𝗻-𝗺𝗲𝘀𝘀𝗮𝗴𝗲: It pulls my business context (programs, voice, audience) before it writes, so it’s not guessing. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 (𝗵𝗶𝗴𝗵 𝗹𝗲𝘃𝗲𝗹): • Stack: TypeScript + Next.js + OpenAI function/tool-calling + a vector database for retrieval + Vercel AI SDK. • RAG: I chunked ~1,000 posts, embedded them, and use semantic search to retrieve 5–10 examples as grounding before it drafts. • Tool-calling orchestration: • Tool A fetches brand/business context (voice, audience, programs). • Tool B retrieves similar writing samples to match style. • Tool C searches my resource library to recommend the right lead magnet. • Guardrails: • Schema validation for structured outputs (titles, hooks, bullets). • Unit tests with an LLM-as-judge to catch regressions in tone and truthiness. • Caching on prompt prefixes to keep latency/cost under control. • Human-in-the-loop: I still review, tighten, and fact-check. It’s a co-writer, not a replacement. If you’re curious about building this kind of thing, start with a small RAG pipeline and add tool-calling later. Keep it boring: get retrieval working reliably, then layer on features like resource suggestions and schema’d outputs. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗵𝗮𝘁 𝗽𝗮𝗶𝗿𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁: • 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗥𝗔𝗚: a practical guide to building retrieval-augmented features in your app https://lnkd.in/gndxjhic	IMAGE	Brian	Jenney	2,480	0	23	12	0	0	0.014113	null	2025-12-16 09:01:39	https://www.linkedin.com/feed/update/urn:li:activity:7406740305170948096
urn:li:activity:7406739938295181312	My most embarrassing interview? That time I ended an interview 10 minutes in when the interviewer asked me a problem I knew I could not solve. I figured I'd save us both some time and awkward chit chat if I just left. So I did. 😢 I vowed to never be in that position again. You see, I sucked at interviewing for years. It ruined my confidence and made me scared to take risks at work. Then I spent thousands of dollars and hundreds of hours learning DSA, system design and reading up on all the parts of JS that I skipped from being mostly self-taught. With my newfound confidence I did around 40 interviews over a year. - 20 mock interviews. - 3 FAANG final rounds. - A dozen non-FAANG. - Too many recruiter screens to count. 𝗥𝗲𝘀𝘂𝗹𝘁: Offer with an increase of ~30K and another one for ~50K. More importantly, a hell of a lot more confidence. In the article below, I'm giving away the material I created that's helped over 100 other developers nail their front end technical interview. Merry Christmas! 𝙄'𝙢 𝙘𝙤𝙣𝙨𝙞𝙙𝙚𝙧𝙞𝙣𝙜 𝙙𝙤𝙞𝙣𝙜 𝙖𝙣 𝟴 𝙬𝙚𝙚𝙠 𝙘𝙧𝙖𝙨𝙝 𝙘𝙤𝙪𝙧𝙨𝙚 𝙤𝙣 𝙉𝙤𝙣-𝙁𝘼𝘼𝙉𝙂 𝙞𝙣𝙩𝙚𝙧𝙫𝙞𝙚𝙬𝙨 𝙩𝙖𝙞𝙡𝙤𝙧𝙚𝙙 𝙩𝙤𝙬𝙖𝙧𝙙𝙨 𝙅𝙎 𝙙𝙚𝙫𝙨. 𝘿𝙈 𝙞𝙛 𝙮𝙤𝙪 𝙬𝙖𝙣𝙩 𝙩𝙝𝙚 𝙙𝙚𝙩𝙖𝙞𝙡𝙨.	ARTICLE	Brian	Jenney	4,367	0	30	3	0	0	0.007557	null	2025-12-16 09:00:11	https://www.linkedin.com/feed/update/urn:li:activity:7406739938295181312
urn:li:activity:7406345758351835136	5 takeaways after 6 months at an AI startup: 1. 𝗜 𝘀𝘂𝗰𝗸𝗲𝗱 𝗮𝘁 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴. ❌ “𝘖𝘱𝘵𝘪𝘮𝘪𝘻𝘦 𝘵𝘩𝘪𝘴 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯” ✅ ”𝘜𝘴𝘦 𝘗𝘳𝘰𝘮𝘪𝘴𝘦.𝘢𝘭𝘭𝘚𝘦𝘵𝘵𝘭𝘦𝘥 𝘢𝘯𝘥 𝘦𝘹𝘦𝘤𝘶𝘵𝘦 𝘵𝘩𝘦𝘴𝘦 𝘱𝘳𝘰𝘮𝘪𝘴𝘦𝘴 𝘪𝘯 𝘣𝘢𝘵𝘤𝘩𝘦𝘴 𝘰𝘧 10” 2. 𝗩𝗶𝗯𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗮𝗺𝗮𝘇𝗶𝗻𝗴… for non-developers. The CEOs vibe coded an amazing mock up with some working code. Great for brain-storming and god-awful for production. 3. 𝗥𝗔𝗚 𝗶𝘀 𝗲𝗮𝘀𝘆 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗲𝗮𝘀𝘆 𝘁𝗼 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝘀𝗰𝗿𝗲𝘄 𝘂𝗽. Chunking and metadata cannot be after thoughts (𝘪𝘧 𝘺𝘰𝘶 𝘥𝘰𝘯’𝘵 𝘬𝘯𝘰𝘸 𝘙𝘈𝘎, 𝘴𝘵𝘢𝘳𝘵 𝘩𝘦𝘳𝘦: https://lnkd.in/d6Pzfcg6) 4. 𝗬𝗼𝘂 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 “𝗮𝗴𝗲𝗻𝘁𝘀”. Try an LLM-powered workflow first. Decide if an AI framework is really worth the complexity. 5. 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱 𝟭𝟬𝘅 𝘄𝗶𝘁𝗵 𝗔𝗜 - when starting from scratch. Productivity slowed in direct proportion to how large/complex the system became. This is why the biggest tech companies are hiring MORE developers, not less. We’re in a weird period as software developers. There aren’t any hard and fast rules when it comes to working with AI. We’re all figuring this out as we go along and learning where the hype ends and reality begins. *** I share more of my spicy takeaways here: https://lnkd.in/ezFJ2H4Q	TEXT	Brian	Jenney	2,180	0	29	5	0	0	0.015596	null	2025-12-15 06:53:52	https://www.linkedin.com/feed/update/urn:li:activity:7406345758351835136
urn:li:activity:7405294122686808065	3 students from Parsity just got hired. They’re talented, amazing people and well deserving. And they’re not much different than you. They felt uncertain. Like it would never happen. That maybe they just weren’t good enough. When I think of all the mentees I’ve seen get hired over the years, there are few hard and fast rules. - Most did NOT learn in public. - Some mass-applied. - A few networked their way to the first role. - Many of their interviews were barely technical. - Some ONLY got LeetCode problems. Your timeline will be unpredictable. Be persistent, practically optimistic that opportunity will present itself, re-calibrate when you see things not working, maintain your skills and your own success will be inevitable.	IMAGE	Brian	Jenney	7,854	0	56	6	0	0	0.007894	null	2025-12-12 09:15:02	https://www.linkedin.com/feed/update/urn:li:activity:7405294122686808065
urn:li:activity:7404926297447129108	Nearly 5 years ago, I made my first 5 figures online by selling interview prep to junior and mid-level developers. Creating that material honestly taught me as much as it helped others. Let me be clear: Interviewing sucks. This is fax. And yet it’s the highest-paying skill you can have as a developer. If you actually finish the assignments and material I can guarantee you'll be more prepared than 99% of bootcamp grads who are interviewing outside Big Tech. The program was really successful: • Ryan got a lead eng position for a well known news org • Sean went from a chaotic start up to Calendly • Eric went from PT to FT with a 13K raise • A young man I met on IG (forget his name) bought a new car after he landed his first role! I'm no longer selling access to this material as I focus on Parsity. Instead of letting this material collect dust, I’m giving it away for free until I come to my senses. If it helps even a few people level up then it's worth it (𝘫𝘦𝘦𝘻 𝘐 𝘧𝘦𝘭𝘵 𝘤𝘰𝘳𝘯𝘺 𝘫𝘶𝘴𝘵 𝘵𝘺𝘱𝘪𝘯𝘨 𝘵𝘩𝘢𝘵 𝘣𝘶𝘵 𝘪𝘵'𝘴 𝘵𝘳𝘶𝘦). 👉 Grab it here: https://lnkd.in/gmfCB5jm	IMAGE	Brian	Jenney	5,371	0	29	4	1	0	0.00633	null	2025-12-11 08:53:26	https://www.linkedin.com/feed/update/urn:li:activity:7404926297447129108
urn:li:activity:7404587023681863681	The human brain builds new neural pathways in as little as a few focused minutes a day. But without structure, feedback, and accountability? Those same pathways weaken just as fast as they're created. 𝗧𝗵𝗶𝘀 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝘀 𝘄𝗵𝘆 𝘀𝗼𝗺𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗼𝗻 𝗮 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗵𝗮𝗺𝘀𝘁𝗲𝗿 𝘄𝗵𝗲𝗲𝗹 𝗮𝗸𝗮 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹 𝗵𝗲𝗹𝗹. Inconsistency becomes the biggest killer of progress. You feel "behind." Get discouraged. Fall out of the habit entirely. 𝗠𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘀𝗽𝗲𝗲𝗱. Once it drops, everything feels harder. Because it is. The fix? Build a system that removes friction and promotes consistency. Systems create predictable results. Motivation does not. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀: 1. Set a minimum daily commitment, even 10 minutes keeps the habit alive 2. 𝗪𝗿𝗶𝘁𝗲 𝗱𝗼𝘄𝗻 𝘆𝗼𝘂𝗿 𝗻𝗲𝘅𝘁 𝘁𝗮𝘀𝗸 𝗯𝗲𝗳𝗼𝗿𝗲 𝗯𝗲𝗱, no more stressing over what to do when you wake up and your kids need pancakes! 3. 𝗧𝗿𝗮𝗰𝗸 𝘆𝗼𝘂𝗿 𝘄𝗶𝗻𝘀, finished lessons, solved bugs, concepts understood 4. 𝗨𝘀𝗲 𝟭-𝟮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 as your learning engine, 𝗻𝗼𝘁 𝟰𝟳 𝗵𝗮𝗹𝗳-𝗳𝗶𝗻𝗶𝘀𝗵𝗲𝗱 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀 5. 𝗚𝗲𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸: mentors, peers, code reviews Do this for 30 days. You'll rebuild momentum, boost confidence, and smooth your learning curve. Simple, effective, repeatable. Easy? Not really.	TEXT	Brian	Jenney	1,574	0	22	3	0	0	0.015883	null	2025-12-10 10:25:16	https://www.linkedin.com/feed/update/urn:li:activity:7404587023681863681
urn:li:activity:7404311957106765824	Stop obsessing over your tech stack. You can honestly just head to Parsity and grab our syllabus to figure out a safe list of tech to learn. That's NEVER been the hard part when it comes to learning to code. To be a hire-able developer, you must build. All you need is 1 project with 5 stages to take you from HTML to JS to AI to AWS and a whole lot of other acronyms in between ⬇️ 𝘐𝘧 𝘱𝘰𝘥𝘴 𝘢𝘳𝘦𝘯'𝘵 𝘺𝘰𝘶𝘳 𝘵𝘩𝘪𝘯𝘨, 𝘫𝘶𝘴𝘵 𝘨𝘳𝘢𝘣 𝘵𝘩𝘦 𝘢𝘳𝘵𝘪𝘤𝘭𝘦 𝘪𝘯 𝘵𝘩𝘦 𝘥𝘦𝘴𝘤𝘳𝘪𝘱𝘵𝘪𝘰𝘯 𝘸𝘪𝘵𝘩 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴, 𝘣𝘰𝘰𝘬 𝘳𝘦𝘤𝘰𝘴 𝘢𝘯𝘥 𝘦𝘹𝘢𝘤𝘵𝘭𝘺 𝘸𝘩𝘢𝘵 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘪𝘯 2026.	ARTICLE	Brian	Jenney	2,984	0	28	3	0	0	0.010389	null	2025-12-09 16:12:16	https://www.linkedin.com/feed/update/urn:li:activity:7404311957106765824
urn:li:activity:7404197525521506307	If you're preparing for a front end interview - read this: Spoke to an acquaintance who works in big tech and just landed a role. He’s done a dozen interviews across startups and a few companies you’ve heard of. What he reported might surprise you: Out of all those technical rounds, he only got 1 DSA problem. Everything else was practical frontend work: • autosuggest with debounce • dashboards with error handling • forms with range queries • requestAnimationFrame timers • pagination • API contract design • React fundamentals He also moved to multiple final rounds after not fully completing the technical challenge. If you’re still grinding LeetCode because you think that’s what frontend interviews look like in 2025 going into 2026, you’re studying for a test many companies aren’t giving.	TEXT	Brian	Jenney	20,779	0	79	17	4	0	0.004813	null	2025-12-09 08:37:33	https://www.linkedin.com/feed/update/urn:li:activity:7404197525521506307
urn:li:activity:7403847184246427648	Too many developers are obsessed with the surface layer when it comes to AI. The fun stuff is just a layer below. Over the last 12 months I've been using AI in production apps which has not only been fun, but has significantly increased interest in my profile. Here's how I'm actually working with these tools: 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀: LLMs can return structured data if you provide a schema. OpenAI supports this out of the box with 𝚣𝚘𝚍. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝗜 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲𝘀: A small tweak to a prompt can break things fast. I use LLM-as-a-judge and unit tests for functions that rely on AI responses. Saves my ass regularly. 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴 𝗺𝗮𝗱𝗲 𝗲𝗮𝘀𝘆: I'm done writing complicated parsing logic. Throw the HTML at an LLM and get a structured response back. There's so much more I'm exploring: RAG, fine-tuning, agents and workflows. It's a fun time to be a software developer.	TEXT	Brian	Jenney	2,021	0	23	2	0	0	0.01237	null	2025-12-08 09:25:25	https://www.linkedin.com/feed/update/urn:li:activity:7403847184246427648
urn:li:activity:7403097226073600000	If you’re a full stack dev and thinking you need to switch into ML - watch this first.	VIDEO	Brian	Jenney	4,287	0	77	8	7	0	0.02146	null	2025-12-06 07:45:21	https://www.linkedin.com/feed/update/urn:li:activity:7403097226073600000
urn:li:activity:7402443193587539968	“𝘞𝘦 𝘴𝘩𝘰𝘶𝘭𝘥 𝘣𝘦 𝘢𝘣𝘭𝘦 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥 𝘵𝘸𝘪𝘤𝘦 𝘢𝘴 𝘧𝘢𝘴𝘵 𝘸𝘪𝘵𝘩 𝘩𝘢𝘭𝘧 𝘵𝘩𝘦 𝘱𝘦𝘰𝘱𝘭𝘦.” That was the promise of AI and it’s failing a lot of developers. Leaders believe the hype. Teams get thrown into AI workflows with no training. And we’re all supposed to “figure it out” from viral posts. Here’s the system that works for me so far: 1. 𝗣𝗹𝗮𝗻 𝗳𝗶𝗿𝘀𝘁. I voice out the idea to ChatGPT to clarify direction. 2. 𝗙𝗶𝗻𝗱 𝗮 𝗽𝗮𝘁𝘁𝗲𝗿𝗻. Either existing examples or one I write myself. 3. 𝗦𝘁𝗮𝗿𝘁 𝘁𝗶𝗻𝘆. Never trust AI with big refactors upfront. 4. 𝗗𝗲𝗾𝘂𝗲𝘂𝗲 𝘁𝗮𝘀𝗸𝘀. Once a pattern works, I feed it more. 5. 𝗥𝗲𝘃𝗶𝗲𝘄 𝗺𝗮𝗻𝘂𝗮𝗹𝗹𝘆. Draft PR, check everything, then commit. When I’m experimenting or stress-testing an idea, I ignore the rules and let AI run wild just to see if the approach has legs. I'm genuinely curious, how are you using Cursor or Claude in your workflow?	TEXT	Brian	Jenney	1,830	0	14	7	0	0	0.011475	null	2025-12-04 12:26:28	https://www.linkedin.com/feed/update/urn:li:activity:7402443193587539968
urn:li:activity:7402429817826304001	Are you a “passionate developer”? Do you need to be? I've met too many "passionate" developers who: • have no commits after graduating • deploy a portfolio site that breaks apart in mobile view • forgot how to write a for loop Amber Adamson and I aren't trying to dunk on junior devs. We honestly just want to see you succeed and too much fluff on LinkedIn is designed to make you feel good instead of winning the game. Link to the episode in comments with some solid ass resources to help you contribute to open source.	VIDEO	Brian	Jenney	2,852	0	34	7	0	0	0.014376	null	2025-12-04 11:33:19	https://www.linkedin.com/feed/update/urn:li:activity:7402429817826304001
urn:li:activity:7402051309841084416	When I decided to learn AI, I started completely in the wrong place. I went straight into ML engineering books, LLM architecture deep dives, and even tried learning how to build a large language model from scratch. As a full-stack developer, that wasn’t the path that helped me understand how to actually use this stuff. Here are 𝟯 𝘀𝘁𝗲𝗽𝘀 that helped me build real intuition ↓ 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗹𝗶𝗻𝗲𝗮𝗿 𝗮𝗹𝗴𝗲𝗯𝗿𝗮 𝗳𝗼𝗿 𝗱𝘂𝗺𝗺𝗶𝗲𝘀 • Get the basics: dot product, cosine similarity, and how vectors actually 𝘣𝘦𝘩𝘢𝘷𝘦. • Search “3Blue1Brown linear algebra” on YouTube. The visuals make everything click. • Understanding vectors will give you some intuition for how step #2 works . 𝟮. 𝗦𝗵𝗶𝗽 𝗮 𝗺𝗶𝗻𝗶 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 • Find some documents you have available, embed them, store vectors in Qdrant or Pinecone, and build an “ask your documents” search. • This alone will give you more intuition than 40 pages of math. 𝟯. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗴𝗲𝗻𝘁 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 • Build a workflow with at least 2 agents that handle separate tasks. • Replicate that same flow using LangGraph (or similar) to understand the problems that agent frameworks solve. • 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: I built a system to search thousands of pieces of content I’ve written and retrieve them by tone, theme, or emotion and built this using different approaches. Most developers read about AI long before they ever 𝘵𝘰𝘶𝘤𝘩 it. It should be the opposite. 𝙄𝙛 𝙮𝙤𝙪 𝙬𝙖𝙣𝙩 𝙨𝙤𝙢𝙚𝙩𝙝𝙞𝙣𝙜 𝙘𝙤𝙣𝙘𝙧𝙚𝙩𝙚 𝙩𝙤 𝙗𝙪𝙞𝙡𝙙, 𝙘𝙤𝙢𝙢𝙚𝙣𝙩 𝙤𝙧 𝘿𝙈 🤖 (𝙨𝙤 𝙄 𝙠𝙣𝙤𝙬 𝙮𝙤𝙪'𝙧𝙚 𝙣𝙤𝙩 𝙖 𝙧𝙤𝙗𝙤𝙩) 𝙖𝙣𝙙 𝙄’𝙡𝙡 𝙨𝙚𝙣𝙙 𝙮𝙤𝙪 𝟮 𝙥𝙧𝙖𝙘𝙩𝙞𝙘𝙖𝙡, 𝙘𝙤𝙙𝙚-𝙝𝙚𝙖𝙫𝙮 𝙚𝙭𝙚𝙧𝙘𝙞𝙨𝙚𝙨 𝙩𝙤 𝙜𝙚𝙩 𝙮𝙤𝙪 𝙢𝙤𝙫𝙞𝙣𝙜.	TEXT	Brian	Jenney	6,617	0	33	10	0	0	0.006498	#2	2025-12-03 10:29:15	https://www.linkedin.com/feed/update/urn:li:activity:7402051309841084416
urn:li:activity:7401689112325599232	Career advice that sounded good on paper but didn't work for me: 1. Don't get involved in office politics 2. You don't need DSA (𝑤ℎ𝑖𝑡𝑒𝑏𝑜𝑎𝑟𝑑 𝑖𝑛𝑡𝑒𝑟𝑣𝑖𝑒𝑤𝑠 𝑎𝑟𝑒 𝑏𝑖𝑎𝑠𝑒𝑑) 3. Stay at each company for 2 years minimum 4. Impostor syndrome is all in your head 5. Hard work will get you promoted As an outsider to tech from an about as non-traditional background as you can imagine - I learned what works for others wouldn't really work for me. Here's some career advice I've gathered over the last 11 years that the LinkedIn mob won't tell you.	ARTICLE	Brian	Jenney	5,904	0	40	21	0	0	0.010332	null	2025-12-02 10:30:01	https://www.linkedin.com/feed/update/urn:li:activity:7401689112325599232
urn:li:activity:7401669124445437952	Better than LeetCode. And free-er too. Every year a large community of developers join Advent of Code for Christmas-themed coding challenges. I usually get through the first week or 2 before I tap out. The first couple days are typically doable for beginners and a good litmus test of your problem solving skills.	IMAGE	Brian	Jenney	2,356	0	45	6	0	0	0.021647	null	2025-12-02 09:10:35	https://www.linkedin.com/feed/update/urn:li:activity:7401669124445437952
urn:li:activity:7401306380122415104	The human brain builds new neural pathways in as little as a few focused minutes a day. But without structure, feedback, and accountability, those same pathways weaken just as fast as they’re created. This explains why 𝘀𝗼𝗺𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗴𝗲𝘁 𝘀𝘁𝘂𝗰𝗸 𝗼𝗻 𝗮 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗵𝗮𝗺𝘀𝘁𝗲𝗿 𝘄𝗵𝗲𝗲𝗹. Inconsistency becomes the biggest killer of progress. You feel “behind,” get discouraged, and fall out of the habit entirely. 𝗠𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘀𝗽𝗲𝗲𝗱. Once it drops, everything feels harder... because it is. So, how do we fix this issue? 𝗕𝘂𝗶𝗹𝗱 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗮𝘁 𝗿𝗲𝗺𝗼𝘃𝗲𝘀 𝗳𝗿𝗶𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆. Systems create predictable results; motivation does not. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝟱 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱: 1. 𝗦𝗲𝘁 𝗮 𝗺𝗶𝗻𝗶𝗺𝘂𝗺 𝗱𝗮𝗶𝗹𝘆 𝗰𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁: even 10 minutes keeps the habit alive. 2. 𝗞𝗲𝗲𝗽 𝗮 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸 𝗯𝘆 𝘆𝗼𝘂𝗿 𝗯𝗲𝗱: write down the next task you plan to do - a tutorial, a small feature, fixing some bug, etc. No more guessing what to do next. 3. 𝗧𝗿𝗮𝗰𝗸 𝘆𝗼𝘂𝗿 𝘄𝗶𝗻𝘀: finished lessons, solved bugs, concepts understood. Leave yourself proof of progress. 4. 𝗨𝘀𝗲 𝟭 𝗼𝗿 𝟮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗮𝘀 𝘆𝗼𝘂𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲 (𝘋𝘔 "𝘴𝘪𝘥𝘦 𝘱𝘳𝘰𝘫𝘦𝘤𝘵" 𝘪𝘧 𝘺𝘰𝘶 𝘸𝘢𝘯𝘵 𝘮𝘺 𝘴𝘪𝘥𝘦 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘤𝘩𝘦𝘢𝘵 𝘴𝘩𝘦𝘦𝘵). 5. 𝗚𝗲𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸: mentors, peers, or code reviews. Find someone with experience to give you feedback on your code Do this and you’ll 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺, 𝗯𝗼𝗼𝘀𝘁 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝘀𝗺𝗼𝗼𝘁𝗵 𝘆𝗼𝘂𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗰𝘂𝗿𝘃𝗲.	TEXT	Brian	Jenney	1,913	0	30	9	0	0	0.020387	null	2025-12-01 09:09:10	https://www.linkedin.com/feed/update/urn:li:activity:7401306380122415104
urn:li:activity:7400597793318457344	This might lose me some business, but it needs to be said: You can't stack AI skills on top of shaky software skills. Before you dive into agents, RAG, or fine-tuning, you need real coding basics. Do this first: • 𝗚𝗲𝘁 𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝘄𝗶𝘁𝗵 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: APIs, error handling, common design patterns and building and deploying full stack apps • 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗜 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲𝘀: Tokens, embeddings, context windows. If those concepts feel fuzzy, everything advanced will feel like magic. Here’s the truth: AI engineering isn’t a shortcut. It’s software development with more moving parts and a rule book that is still being written. If you don’t have a solid foundation you'll be building on a house of cards.	TEXT	Brian	Jenney	33,801	0	182	40	13	0	0.006952	null	2025-11-29 10:13:30	https://www.linkedin.com/feed/update/urn:li:activity:7400597793318457344
urn:li:activity:7400242989056417792	Here are 4 services I don't regret buying that no one sponsored me to tell you: 1. https://lnkd.in/guxkUu3e (𝗔𝗹𝗴𝗼𝗘𝘅𝗽𝗲𝗿𝘁) - AlgoExpert is my go to for studying DSA and prepping for FE interviews 2. https://lnkd.in/gc-vexCT (𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝘃𝗲) - Educative's Grokking the Interview exposes the patterns behind 99% of the problems you will encounter in whiteboard interviews 3. https://www.jointaro.com/ (𝗝𝗼𝗶𝗻𝗧𝗮𝗿𝗼)- expert career advice and a true community for developers who want practical guidance 4. https://codecrafters.io/ (𝗖𝗼𝗱𝗲𝗖𝗿𝗮𝗳𝘁𝗲𝗿𝘀) - you build real software using git to submit your work. For senior devs who are tired of long ass videos (check out John Crickett as well) 5. https://parsity.io/dev30 (𝗗𝗲𝘃 𝟯𝟬) - a 30 day program to learn JS for real and build in public That 5th one is my own program 😉. 𝗜'𝗺 𝗱𝗼𝗶𝗻𝗴 𝗮𝗻 𝗼𝗹𝗱 𝘀𝗰𝗵𝗼𝗼𝗹 𝗕𝗹𝗮𝗰𝗸 𝗙𝗿𝗶𝗱𝗮𝘆 𝘀𝗮𝗹𝗲 𝘁𝗼𝗱𝗮𝘆. 𝗡𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗮𝘁 𝟭𝟬% 𝗻𝗼𝗻𝘀𝗲𝗻𝘀𝗲. Remember this: nearly any course will work for you if you actually go through it. I've used and paid for every resource I listed above and genuinely like them. Hope that's helpful!	TEXT	Brian	Jenney	4,225	0	21	5	0	0	0.006154	null	2025-11-28 10:43:38	https://www.linkedin.com/feed/update/urn:li:activity:7400242989056417792
urn:li:activity:7399476634149560320	Most junior devs don’t realize this: 𝘆𝗼𝘂𝗿 𝘀𝗶𝗱𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘀𝗮𝘆𝘀 𝗺𝗼𝗿𝗲 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗹𝗲𝗮𝗿𝗻 𝘁𝗵𝗮𝗻 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄. The fastest learners aren’t building "simple" projects - they’re building projects that force them into unknown territory. You can grow more from one intentionally over-engineered project than from 10 polished tutorials. 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗱𝗲𝗳𝗮𝘂𝗹𝘁 𝘁𝗼 𝘀𝗮𝗳𝗲 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀 𝗼𝗿 𝘁𝗶𝗻𝘆 𝗮𝗽𝗽𝘀, 𝘆𝗼𝘂 𝗻𝗲𝘃𝗲𝗿 𝘀𝘁𝗿𝗲𝘁𝗰𝗵 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀. As a result, you never experience the real problems that make you job-ready. The fix: 𝗕𝘂𝗶𝗹𝗱 𝗼𝗻𝗲 𝘀𝗶𝗱𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗯𝗶𝗴 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝘆𝗼𝘂 𝗮𝗻𝗱 𝗼𝘃𝗲𝗿-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝘁 𝗼𝗻 𝗽𝘂𝗿𝗽𝗼𝘀𝗲. Complexity creates a large surface area to learn what you’re missing. Here are 𝟰 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱: • Choose one project that feels slightly out of your depth. • Pick a tech stack you 𝘸𝘢𝘯𝘵 to learn, not the one you already know. Use 80% familiar tech and 20% new. • Add deliberate complexity to one layer (backend, deployment, architecture). • Treat every blocker as the curriculum - not a reason to quit. Take 30 days to follow this approach and you’ll 𝗯𝘂𝗶𝗹𝗱 𝘀𝗸𝗶𝗹𝗹𝘀 𝘁𝗵𝗮𝘁 𝗻𝗼 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹 𝗰𝗮𝗻 𝗴𝗶𝘃𝗲 𝘆𝗼𝘂.	IMAGE	Brian	Jenney	4,683	0	29	11	1	0	0.008755	null	2025-11-26 07:58:25	https://www.linkedin.com/feed/update/urn:li:activity:7399476634149560320
urn:li:activity:7399108472203722752	Can I admit something? I didn't really get MCP... So today I sat down and created a small MCP server and integrated it with Claude. This server exposes a couple tools that Claude can use to search EVERY piece of content I've written by querying a vector database and then apply that context to write a Linkedin post or article using my personal experiences and tone. 𝗦𝘁𝗲𝗽 𝟭: Chunk and store all my posts and articles into a vector DB (I used Qdrant) 𝗦𝘁𝗲𝗽 𝟮: Use @𝚖𝚘𝚍𝚎𝚕𝚌𝚘𝚗𝚝𝚎𝚡𝚝𝚙𝚛𝚘𝚝𝚘𝚌𝚘𝚕/𝚜𝚍𝚔 library to create and expose the tools in a Node/Express app 𝗦𝘁𝗲𝗽 𝟯: Run the server locally and use 𝚗𝚐𝚛𝚘𝚔 to expose it to publicly (𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘱𝘦𝘦𝘱𝘴 𝘱𝘭𝘦𝘢𝘴𝘦 𝘥𝘰𝘯'𝘵 @ 𝘮𝘦) 𝗦𝘁𝗲𝗽 𝟰: Add the ngrok endpoint as a custom connector in Claude (𝘢𝘨𝘢𝘪𝘯, 𝘴𝘦𝘤𝘶𝘳𝘪𝘵𝘺 𝘧𝘰𝘭𝘬𝘴, 𝘥𝘰𝘯'𝘵 𝘩𝘢𝘵𝘦 𝘮𝘦 𝘱𝘭𝘻) 𝗦𝘁𝗲𝗽 𝟱: 😎 While the AI bros and normies continue to freak out about AGI and AI taking over our jobs - I'd encourage you to actually play with some of the tech out there. This has got to be one of the most fun times to be writing code.	IMAGE	Brian	Jenney	7,984	0	58	9	0	0	0.008392	null	2025-11-25 07:35:28	https://www.linkedin.com/feed/update/urn:li:activity:7399108472203722752
urn:li:activity:7398752313202552832	The developer community needs less: - 1,000-hour “free” YouTube epics you’ll never finish - Interview grind content aimed at the top 1% of big tech - “𝘋𝘢𝘺 𝘪𝘯 𝘵𝘩𝘦 𝘓𝘪𝘧𝘦” vlogs (aka layoff teasers) that glamorize the job but don’t teach it And more: - Hands-on GitHub repos you actually clone, break, and fix - Text-based guides you can skim, search, and return to when you’re stuck - Honest write-ups of outages, failures and on-call shifts. Not just highlight reels Most junior devs don’t need more entertainment. They need practical reps with real code, real constraints, and real problems.	TEXT	Brian	Jenney	12,235	0	57	27	2	0	0.007029	null	2025-11-24 08:00:13	https://www.linkedin.com/feed/update/urn:li:activity:7398752313202552832
urn:li:activity:7398127410350575617	Brb doing hood rat stuff.	VIDEO	Brian	Jenney	1,108	0	18	5	0	0	0.020758	null	2025-11-22 14:37:05	https://www.linkedin.com/feed/update/urn:li:activity:7398127410350575617
urn:li:activity:7397705944144572416	I’ve worked with hundreds of career changers and early career developers. Here’s how the successful ones operate: They do the uncomfortable stuff. • Reach out to real people in their 𝗿𝗲𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. Not JUST online. • 𝗕𝗼𝗺𝗯 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀, learn from them, and move on. Not crawl in a code hole of despair. • 𝗕𝘂𝗶𝗹𝗱 𝟭 𝗼𝗿 𝟮 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 instead of a dozen portfolio projects. • They're 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰. They accept things will suck but not forever. This is how you can become a hire-able developer without being the smartest person in the room. • If you want confidence, then collect proof of what you learn. Record yourself. Journal. Push code. • If you want a network, then talk to people online and in person. • If you want opportunities, then do the things you avoid. Simple, effective, repeatable. Easy? Not really. I’ve seen this pattern for years: the people who win aren’t always the most talented. They’re the ones who stay curious, build cool shit, stay flexible, and refuse to give up.	TEXT	Brian	Jenney	3,424	0	45	15	2	0	0.018107	null	2025-11-21 10:42:19	https://www.linkedin.com/feed/update/urn:li:activity:7397705944144572416
urn:li:activity:7397316816450154497	How old is too old to learn to code? According to the internet... it's 30. Thank god Nils didn't take this advice. When Nils came to Parsity, he was 44, switching careers, and had no idea how to break into tech. Six months later? 𝗛𝗲’𝘀 𝗮 𝗳𝘂𝗹𝗹-𝘁𝗶𝗺𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘄𝗶𝘁𝗵 𝗮 𝗵𝘆𝗯𝗿𝗶𝗱 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲, 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝗵𝗶𝘀 𝗳𝗮𝗺𝗶𝗹𝘆, 𝗮𝗻𝗱 𝗮 𝗰𝗹𝗲𝗮𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗽𝗮𝘁𝗵. Here’s what changed: 𝟭. 𝗛𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗱 𝗮 𝗿𝗲𝗮𝗹 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗻𝗼𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗰𝗵𝗮𝗼𝘀) • Started with Codecademy • Joined Parsity for a guided curriculum and mentorship • Treated coding like music: patterns, repetition, feedback 𝟮. 𝗛𝗲 𝘂𝘀𝗲𝗱 𝗵𝗶𝘀 𝗿𝗲𝗮𝗹 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 (𝗻𝗼𝘁 𝘁𝗵𝗲 𝗷𝗼𝗯 𝗯𝗼𝗮𝗿𝗱𝘀) • Reached out to former colleagues • Got contract work through a local connection • Landed his full-time role through a personal referral 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 • Full-time software engineer • Moved his family to Kansas City • Flexible hybrid schedule • Supportive senior devs + mentorship • Clear path to leadership Nils didn’t get lucky. He followed the right process and stayed consistent. If you’re a busy adult and want to break into tech without wasting years... DM me “𝗦𝗧𝗔𝗥𝗧.” I’ll send you the exact steps Nils followed.	TEXT	Brian	Jenney	27,310	0	76	31	0	0	0.003918	null	2025-11-20 08:56:04	https://www.linkedin.com/feed/update/urn:li:activity:7397316816450154497
urn:li:activity:7396973123205554176	5 biggest mistakes of my coding career (so far): 1. Being afraid to admit when I didn’t know something 2. Not learning the fundamentals before diving into frameworks 3. Only taking on tasks I knew I could finish 4. Not understanding how code fits into the company eco-system and business goals 𝟱. 𝗡𝗼𝘁 𝘀𝗽𝗲𝗮𝗸𝗶𝗻𝗴 𝘂𝗽 That last one hurt me the most. I thought I was playing it safe by taking on easy tickets. I nodded my head during estimation sessions and gave bland status updates. I never shared my ideas during meetings. I wanted to blend in. It was the most dangerous thing I could’ve done. There's a saying - "𝘵𝘩𝘦 𝘵𝘢𝘭𝘭𝘦𝘴𝘵 𝘣𝘭𝘢𝘥𝘦 𝘰𝘧 𝘨𝘳𝘢𝘴𝘴 𝘪𝘴 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘵𝘰 𝘨𝘦𝘵 𝘤𝘶𝘵." It’s also the one growing the fastest. Companies need average developers more than they’d like to admit. But, if career trajectory and increased hire-ability is your goal, then playing it safe is your greatest threat.	TEXT	Brian	Jenney	12,204	0	44	14	0	0	0.004753	null	2025-11-19 10:10:21	https://www.linkedin.com/feed/update/urn:li:activity:7396973123205554176
urn:li:activity:7396641154655518720	Let's go old school. No vibe coding. No AI. We're going to plan out your next side project using a process I use today when I have no clue what to make. Over the years my side projects have helped me switch tech stacks, made me some money and kept my skills relevant. If you have no clue what to build - by the end of the workshop you'll have a plan of action that will NOT suck.	EVENT	Brian	Jenney	5,667	0	42	9	0	0	0.008999	null	2025-11-18 12:11:14	https://www.linkedin.com/feed/update/urn:li:activity:7396641154655518720
urn:li:activity:7396618867868909568	I’ve been posting on LinkedIn for 5 years but I don’t know if I’ve ever formally introduced myself. I’m Brian, a 41-year-old software developer from Oakland, California. Here’s my story so far: 𝟮𝟬𝟭𝟯 Got sober after an intervention. It was bad. Suddenly had time. A lot of it. No idea what life looked like next. 𝟮𝟬𝟭𝟰 Learned to code to fill the hours. Got my first dev job at Grocery Outlet. Made peanuts. Didn’t matter. I was getting paid to do what I loved. 𝟮𝟬𝟭𝟳 I got greedy. Left for a startup. Doubled my salary. Anxiety, crippling doubt. Realized how little I knew. 𝟮𝟬𝟭𝟳 - 𝟮𝟬𝟭𝟵 Another startup. Study my ass off to fill in my technical gaps. Promoted to senior developer. Tried building my own startup and failed miserably. Switched stacks from the failed startup project. 𝟮𝟬𝟭𝟵–𝟮𝟬𝟮𝟯 Moved again - this time to Clorox (the bleach company!). Eventually managed a team. Made the presentations. Hired. Fired. Managing! Started quietly building an online business. Laid off 😅 𝟮𝟬𝟮𝟱 Pivot to AI startups Go from manager to senior software engineer. No ragrats. And I own Parsity. A coding bootcamp… in 2025. Today, we help around 30 people a year break into software, learn full-stack development, and build actual AI engineering skills — not “prompt engineering,” but the stuff companies actually hire for. 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝗿𝗲𝗮𝗸 𝗶𝗻𝘁𝗼 𝘁𝗲𝗰𝗵: I make a ton of free resources for early learners. Everything is in my bio. If you want to apply to Parsity, you’ll find that there too. 𝗡𝗲𝘅𝘁 𝘆𝗲𝗮𝗿 𝗺𝘆 𝗴𝗼𝗮𝗹 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: Help a handful of people make a real transition into software and learn the AI skills that will define the next few years.	TEXT	Brian	Jenney	7,471	0	104	17	2	0	0.016464	null	2025-11-18 10:42:40	https://www.linkedin.com/feed/update/urn:li:activity:7396618867868909568
urn:li:activity:7396302215562391553	We got to do better... as a people. Introducing the first brain-rot code editor. For developers who get bored while waiting for AI-generated code. You can find matches on dating sites, gamble or doom scroll! No, April Fool's has not come early. Yes, YC is backing them. F*ck yeah, this is great marketing. Maybe, I'm just getting too old for this shit.	IMAGE	Brian	Jenney	2,335	0	21	10	0	0	0.013276	null	2025-11-17 13:44:24	https://www.linkedin.com/feed/update/urn:li:activity:7396302215562391553
urn:li:activity:7396236802749083649	No one wants to be late to the AI party but there is a price to pay. 𝗧𝗵𝗲 𝗲𝗮𝗿𝗹𝘆 𝗮𝗱𝗼𝗽𝘁𝗲𝗿 𝗽𝗲𝗻𝗮𝗹𝘁𝘆 𝘁𝗮𝘅. There’s no clear winner when it comes to patterns, tools and most importantly… outcomes. You see this play out on social media. There’s some guy (it’s always a guy) who has THE workflow to 10x! It’s the 1 you’re not using. 𝘖𝘩, 𝘵𝘩𝘦 𝘈𝘐 𝘵𝘰𝘰𝘭 𝘺𝘰𝘶’𝘳𝘦 𝘶𝘴𝘪𝘯𝘨 𝘬𝘪𝘯𝘥𝘢 𝘴𝘶𝘤𝘬𝘴? 𝘋𝘰𝘯’𝘵 𝘸𝘰𝘳𝘳𝘺, 𝘵𝘩𝘦𝘳𝘦’𝘴 𝘢 𝘵𝘰𝘰𝘭 𝘺𝘰𝘶 𝘤𝘢𝘯 𝘢𝘥𝘥 𝘰𝘯 𝘵𝘰𝘱 𝘰𝘧 𝘵𝘩𝘢𝘵! (𝘪𝘵'𝘴 𝘰𝘯𝘭𝘺 20 𝘣𝘶𝘤𝘬𝘴 𝘢 𝘮𝘰𝘯𝘵𝘩!) 𝘖𝘰𝘧, 𝘵𝘩𝘢𝘵 𝘱𝘳𝘰𝘮𝘱𝘵 𝘪𝘴 𝘵𝘰𝘰 𝘰𝘱𝘦𝘯-𝘦𝘯𝘥𝘦𝘥. 𝘞𝘢𝘪𝘵, 𝘵𝘩𝘢𝘵 𝘱𝘳𝘰𝘮𝘱𝘵 𝘪𝘴 𝘸𝘢𝘺 𝘵𝘰𝘰 𝘷𝘦𝘳𝘣𝘰𝘴𝘦. 𝘈𝘩𝘩, 𝘺𝘰𝘶’𝘳𝘦 𝘶𝘴𝘪𝘯𝘨 𝘵𝘩𝘦 𝘸𝘳𝘰𝘯𝘨 𝘮𝘰𝘥𝘦𝘭 𝘧𝘰𝘳 𝘵𝘩𝘢𝘵 𝘵𝘢𝘴𝘬. This is a very messy time to write code. It’s frustrating. It’s tiring. It’s also fun. We’re making up the rules as we go along.	TEXT	Brian	Jenney	3,674	0	16	5	0	0	0.005716	null	2025-11-17 09:24:29	https://www.linkedin.com/feed/update/urn:li:activity:7396236802749083649
urn:li:activity:7394781134074597376	Most tutorials on building AI agents: - Python-centric - Way too high level - Use frameworks prematurely - Would not work for non-trivial use cases I wrote this article which includes a full stack web app with some missing functionality for you to code so you can learn how to PRACTICALLY build agents. In January, I'll be leading a 30 day cohort for full stack devs who want to learn AI beyond prompting. DM me 🤖 if you're interested - there are 6 spots left.	ARTICLE	Brian	Jenney	1,918	1,499	16	5	0	0	0.010949	null	2025-11-13 09:00:10	https://www.linkedin.com/feed/update/urn:li:activity:7394781134074597376

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