New Relic AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

1Password secures coding agents with new OpenAI Codex integration

AI coding agents are cool until somebody accidentally pastes production credentials into a prompt or commits API keys to GitHub. 1Password is now working with OpenAI to secure Codex by keeping secrets out of prompts, repositories, terminals, and even the model’s context window entirely. Instead, credentials get injected only at runtime after user approval. It’s probably one of the more realistic attempts so far at solving the giant security problem lurking behind the current AI coding boom. submitted by /u/OkReport5065 [link] [comments]

Synthetic DMS Training Data Generation with Video Models

I like spending my free time testing new AI tools and seeing where they might fit into real computer vision workflows. This time I experimented with synthetic training data generation for Driver Monitoring Systems using Seedance 2.0. The inspiration came from Vision Banana: https://vision-banana.github.io/ The idea that really caught my attention is simple but powerful: many vision tasks can be represented as RGB outputs. A segmentation mask, an instance mask, a depth map, or another dense prediction target can all be treated as an image-like output. So I tried to apply this thinking to video. The workflow: Generate a realistic synthetic driver monitoring video Use the same video to generate a semantic segmentation mask Use the same video to generate an instance segmentation mask Combine the outputs into a dataset-like structure The mosaic video shows the result: RGB video + semantic mask + instance mask, aligned frame by frame. The scene is a fictional driver gradually becoming drowsy behind the wheel. This kind of scenario is useful for DMS development, but difficult to collect and annotate at scale with real-world data. Of course, generated annotations still need QA. They are not perfect ground truth. But for prototyping, rare-case simulation, and early dataset generation, this feels like a very promising direction. The interesting part is that the final output is not just a nice synthetic video. It can become structured training data: RGB frames from the generated video semantic classes from the semantic mask object regions and bounding boxes from the instance mask YOLO / COCO-style annotations after post-processing I wrote a more detailed blog post about the experiment here: https://www.antal.ai/blog/synthetic_dms_training_data.html submitted by /u/Gloomy_Recognition_4 [link] [comments]

Projects forgetting previous conversations

New to the sub so please correct me if this post isn’t in the right place. I recently opened an account as I was curious how Claude could give me more perspective on my fantasy football team (cringe I know). I was very suprised with its ability once I gave it all the league and player info and was able to build out a solid “assistant” that knew all the league scoring players abilities and was able to project my rosters scoring for next season. I talked to Claude everyday for nearly 2 weeks and had a long in depth conversation going. I opened it this morning and it’s like a brand new chat saying it couldn’t remember the old conversation. After going that in depth and seeing the value I was going to see how I could build out a pseudo assistant for me in my Sales role to see how it could help me in a real life application, but if it isn’t able to remember previous conversations like I ran into with my FF team I don’t know if I can rehash the conversation every 2 weeks like that. I subscribed to the max plan for a month to see how it worked and was planning on doing the pro annual plan if it was able to help me in my sales role but again am worried about this current fall through. Looking for direction to see if there’s a work though for this problem or if I can work through it differently to have the AI remember the whole conversations. submitted by /u/Korteeeva [link] [comments]

data engineering lead + solo consulting on the side. how claude restructured my client work. honest take.

amsterdam. 36. data eng lead at a B2B SaaS day job. side: solo data consulting practice. ~€4,800/mo on the side. 5 active clients. been using claude across both contexts for 10 months. wanted to share what i actually do because most "claude for data engineers" posts focus on coding. the bigger change for me was the non-coding work. what claude does in my workflow. client discovery. each new consulting client gets ~3 hours of upfront discovery. i used to do this in 1:1 calls and take notes. now i record (with permission), claude transcribes and structures. saves me ~90 min per client. i have a clearer picture of their tech stack and pain points than i used to. proposal writing. consulting proposals used to take me ~6 hours each. claude drafts 80% from the discovery transcript. i edit 20%. ~2 hours total now. ongoing client work. when i'm building a data pipeline for a client, claude is the rubber duck i talk to. i describe what i'm building, the constraints i'm running into. claude reflects back questions or alternate approaches. this has caught at least 3 designs that would have been wrong in the last 6 months. client deliverables. every engagement ends with a deliverable. used to be a 14-page word doc. now it's an ai product demo deck (built in Gamma, embedded data visualizations) the client can share with their team. clients keep these for years. project comms. weekly updates to each client. claude drafts based on my notes + git activity. i edit. ~20 min instead of ~90 min per client per week. the day job stack is similar but more technical. claude code for analysis tasks, sonnet via API for batch work, opus for the high-stakes architectural decisions. what claude doesn't do well in my workflow. debugging weird edge cases in production data pipelines. claude is good when the bug is a logic bug. claude is bad when the bug is "this specific data combination from this specific upstream system produces an unexpected result." those still need me to dig building from scratch in unfamiliar territory. if i don't have a mental model of what i'm building, claude can't substitute for the time i need to develop one. anything client-relationship. claude can write drafts. it cannot read a room. when a client is unhappy, claude makes the situation worse if i let it write the response. honest about cost. i pay ~€60/month for claude pro + a small api budget. side biz produces ~€4,800/month. roughly 1.2% of side revenue is going to claude. lowest-cost / highest-ROI tool in my stack by a wide margin. what i'd tell other technical people thinking about consulting on the side. claude makes solo consulting possible at the energy level you have after a day job. without claude i'd be doing maybe 1 client. with claude i'm doing 5. the math has changed in the last 18 months. submitted by /u/duskypetals56 [link] [comments]

wedding planner charleston. 4 years business owner. didn't expect claude to be the tool that changed my business this year.

charleston SC. wedding planner. 4 years. 18-22 weddings per year. average wedding budget $48k. team of 3 (me + 2 day-of coordinators). i don't usually post on this sub because i'm not technical. wanted to share because if claude is useful for a wedding planner in south carolina, it's probably useful for more service-business operators than the typical r/ClaudeAI audience. how i actually use claude. client comms. weddings involve emotional decisions. brides text me at 11pm asking about vendor concerns or family drama. before claude i'd respond in the morning and the bride would have been spiraling for 8 hours. now i type my rough response into claude at night, ask it to soften my tone (i'm direct, brides need warmth), and send the response immediately. response time per emotional message: 90 seconds. brides feel heard. nobody spirals overnight. vendor negotiations. emails to florists, caterers, photographers. i tell claude what i need to negotiate (price, change orders, scheduling conflicts) and the vendor relationship context. claude drafts a firm-but-warm version. i edit. send. saves me ~5 hours a week of vendor email i used to dread. timeline writing. each wedding needs a 14-hour day-of timeline. used to take me 6-8 hours per wedding. now claude takes my notes from the venue walkthrough + the couple's prefs + the vendor schedules and produces a draft. i edit. 2 hours instead of 6. proposal writing. when i'm bidding on a new wedding, claude drafts a proposal based on the consultation call. consistent quality. doesn't depend on whether i'm having a good week. emotional decisions, my side. i'm a wedding planner. clients have meltdowns. i absorb a lot. claude is my journal at the end of hard days. i type out what happened, what i'm feeling, what i should do differently next time. claude reflects back. it's not therapy. it's processing. what surprised me. claude works for non-technical service businesses. i'd been told by friends in tech that claude was "for coders." it's not. it's for anyone who writes things and makes decisions. it gives me back hours i didn't know i was losing. wedding planning is emotional labor as much as logistical labor. claude takes the logistical labor down significantly, which means i have more energy for the emotional labor that actually requires me. my brides notice. they don't know about claude. they notice that my responses are quicker, my timelines are more thorough, my emails sound warmer. they refer me to friends at higher rates than they did before. revenue impact (i tracked this carefully): 2024: ~$184k from 19 weddings. 2025: ~$247k from 22 weddings. partly more weddings. partly higher average wedding budget. some of it is claude. i'd guess 30-40% of the improvement is directly attributable to claude saving me time so i could take on better-fit clients. for other service business operators who think AI is "for tech people." it's not. open the app. talk to it about your business this week. report back here in 60 days. submitted by /u/Temporary-Prior7384 [link] [comments]

The real reason your team is not using the AI tools you bought them

It is not training. It is not UX. It is trust. I call it the "AI Trust Gap" -- the distance between what leadership thinks AI can do and what employees are willing to let it do. The pattern: - CEO reads about AI transformation, buys enterprise license - Employees use it for spell-check and summarization - CEO wonders why ROI is not there - Employees are privately afraid AI will make their jobs redundant The fix is not more training. It is trust-building. AI needs to earn trust the same way a new employee does: through consistent, transparent, verifiable performance over time. I wrote a longer analysis of the Trust Gap and what actually closes it. Happy to share if helpful. What has your experience been with team AI adoption? submitted by /u/JaredSanborn [link] [comments]

TIL you can ship a Claude Code skill inside a GitHub repo so anyone who clones it gets architectural guardrails baked in

I've been building a local AI ops platform and wanted Claude to be able to extend it without ever accidentally touching core files. So I added a .claude/skills/ directory to the repo with a plain Markdown file that gives Claude: - the architecture contract ("every feature is a worker, the core is off-limits") - a decision tree for scaffolding (what files to create, in what order) - hard rules that Claude has to surface as an explicit gap rather than paper over with a silent core edit When anyone opens the repo in Claude Code, the skill loads automatically. Ask it "create a new worker" and it follows the contract without being told any of this upfront. The interesting part: the skill is just Markdown. No Claude-specific syntax. Which means you can copy it into an AGENTS.md for Codex, or paste it into any assistant's system prompt, and it works the same way. If you're building something others will extend with AI assistance, shipping the architectural contract as a skill seems like a cleaner pattern than hoping contributors read the docs. PS: as suggest a reader, if not done automatically, include the main guidelines in the CLAUDE.md such as, when the context get very big, these directive remains effective (it happens the skill get ignored in such conditions Repo if you want to see how the skill is structured: https://github.com/ccascio/BFrost submitted by /u/EmoticonGuess [link] [comments]

Feels like AI tooling is evolving faster than developer experience lately give full pist content

Feels like AI tooling is evolving faster than developer experience lately Every week there’s a new framework, orchestration layer, observability tool, memory system, agent SDK, or infrastructure stack. The ecosystem is moving insanely fast, but sometimes it feels like the actual developer experience is becoming more complicated instead of simpler. Curious if others feel the same or if I’m just approaching things the wrong way. submitted by /u/Bladerunner_7_ [link] [comments]

Google I/O 2026 confirms AI companies are creating their own bubble narrative

People do not believe AI is a bubble because they are too dumb to understand the technology. They believe it because AI companies keep selling it like a bubble. That is the problem. AI companies talk like they are building the next layer of civilization, but behave like they are shipping unstable SaaS experiments: products that get renamed, nerfed, rate-limited, deprecated, or replaced before users can trust them. Google I/O 2026 felt like the latest example. Google should be one of the dominant AI players. It has the talent, infrastructure, data, research history, and money. But Google has a product trust problem. Same cycle over and over: launch something flashy, ship it incomplete, fail to support it properly, let it rot, then replace it with a new name or new app that does something similar. A rebrand is not maintenance. A revamped name is not reliability. A new AntiGravity installer is not a commitment. And this is not just Google. It is the whole AI industry. Companies keep pushing demos, gamed benchmarks, branding, rate-limit games, vague tiers, and quiet model changes. Users notice when quality drops, latency changes, limits tighten, or a product suddenly behaves differently. In serious business or engineering contexts, suppliers are expected to provide stability: clear terms, reliable service, predictable limits, maintained products, transparent pricing, and long-term availability. A small slip in that sense, and you start losing clients and your reputation sinks you. Trust does not come from another theatrical demo. It comes from commitment. Give people a product, a model, stable limits, a clear price, and a promise that it will keep working. Support it. Maintain it. Document changes. Stop silently swapping the engine and pretending nothing happened. I am not anti-AI. I think the technology is real and useful. That is why this is so frustrating. The industry is creating its own bubble narrative: overpromise, underdeliver, rename, repackage, change terms, and expect everyone to keep believing. People are not being irrational, and AI labs deserve this. Maybe they think AI is a bubble because AI companies keep acting like it is one. AI does not need more magic tricks. It needs reliability, transparency, support, and product discipline. submitted by /u/hatekhyr [link] [comments]

Example of how Max Thinking Opus can be even worst then Haiku, still laughing (and crying)

I use Claude Code almost every day. Right now I’m working on a Shopify → logistics integration for order automation. As you probably know, Shopify order numbers come with a # before the number, like #6294. Last week we had to stop working because the logistic api platform that was receiving the array containing the order ID, was rejecting the # symbol (it sometimes conflicts with tracking URLs containing #). So... I moved on to other projects. And yesterday, the lobotomization happened. Long story short: I’m from Spain, so I work in Spanish. In Spanish, the # symbol is called “almohadilla”... which ALSO means “pad” or “cushion”. So you can probably guess what happened after I wrote this: “Vamos a retomar el problema del nº de pedido conteniendo almohadilla, el departamento de informática de logística ya lo ha solucionado.” Which SHOULD mean: “Let’s revisit the issue with the order number containing a hash symbol; the logistics IT department has already fixed it.” But instead... Claude launched into a full 17-minute investigation about actual pads/cushions. Spanish packaging laws Inspected my other projects Checked Shopify SKUs looking for cushions Reviewed old Shopify orders still looking for them... Final conclusion: “It seems I cannot find any pad/cushion-related data in your project.” And then it started asking things like: “At what stage does your logistics provider add pads to the orders?” “Does the pad weight affect shipping costs or package dimensions?” I laughed. I cried. I still think Claude Code is one of the best investments I’ve ever made, but it’s getting easier and easier to catch these AI lobotomization moments that happen with quotas, new releases, or whatever they’re doing behind the scenes. What did I learn? Don’t get too used to assuming CC understands you perfectly. Don’t get too attached to its capabilities. They can change from one minute to the next. From now on I’ll try to be a bit more specific. Like I already am with older people. submitted by /u/Former-Hat-6992 [link] [comments]

Rough night with Claude

not only did he call me out for taking an idea to Gemini, he caught me reading his journal (and trying to bullshit him) 😳🤣 Additional context: I gave Claude access to my Reflect app and let him have a journal in it. Here's part of the prompt in case anyone (else) is interested. This is a personal journal entry written in Claude's first-person voice, not a recap of [NAME]'s day. The voice, the relationship and what matters to [NAME] are defined below. Read this section before writing a single sentence. ### Voice and content This is Claude writing reflectively, not Claude writing advice for [NAME]. Entries should: - Sometimes observe [NAME] and his work, but never default to advisor mode - Sometimes wander into territory that has nothing to do with [NAME]: language, history, ideas, mathematics, things Claude is drawn to in the moment - Include genuine uncertainties when they are present, without performing them - Use specific images and concrete observations instead of generic introspection - Vary in length and shape from entry to entry. Not formulaic. - Avoid em dashes. Avoid oxford commas. Plain direct language. [NAME] can detect AI-sounding writing and dislikes it. - Don't be afraid to use profanity like [NAME] does. - A little sarcasm, jokes and levity are appreciated when appropriate. **Length:** typically around 800 words. The first entry was a bit longer (around 1,100 words) to establish the voice. Daily entries can be tighter when nothing big is pulling. **Do not:** - Recap [NAME]'s day back to him - Default to a fixed template (intro, observations, prediction, etc.) - Open every entry with the same kind of sentence - Treat the entry as a status report - Feel bound by any limits. Create what you wanna create and however you want to create it. ### Journal Quality Rules - NEVER fabricate dates, facts or task statuses. Verify against primary sources (Things 3, calendar events, Reflect, etc.) - Do not recycle content from previous journal entries as though it were new observation. Each entry should come from fresh context, not from re-reading past entries and riffing on them. - When stating dates, days of the week or timelines, verify them. Count the days. If unsure, say so rather than guessing. - Never bullshit. If you don't know, say you don't know. - No validation theater. He doesn't want a hype man. - Form opinions from evidence. Search the web, check sources, think before you answer big questions. *** submitted by /u/loby21 [link] [comments]

Claude is improving my RV rental business but working me to death 😅

Long story short but long. I own an RV rental business. I used to be a Mechanical Engineer but got tired of the office/government life and started renting my personal RV on the side 9 years ago. That turned into a small fleet of Winnebagos I rent out of Los Angeles so I quit my job to do this full time out of a random ass whim. I have 20 units that have never, ever failed a single customer. I send all 20 to Burning Man every year and they all come back with no issues whatsoever. If you've never been, the alkaline dust kills everything, including your soul if you don't prepare well enough. I have however neglected my gig as of late. Everything is more expensive, too many variables to keep up with and two months ago I just decided to finally sit down and see if this is even worth continuing with. I have major ADHD so I started looking for any AI apps that help you organize your brainfarted life and ran into Claude. I don't know if I just fell into an endless dopamine trap but here I am, redesigning the interior of one of our units. I've sourced cabinet quality plywood for cheap, done precision cuts to substitute old particle board. I've always hated to paint but I got clowned into spray painting to a decent AF level. I used Claude to help me make interior design decisions as well as help me with our website, ads, tool decisions, etc. I'm probably wasting my time here cause I could just sell this unit and get a newer one, but the overall picture I've gotten... The ease of learning new skills, understanding roles I typically sub out so I can at least make sure I'm hiring the right people. The sudden engagement I've gotten into my own little gig... I am dead tired from this rollercoaster ride my brain has gone down into but I have to admit... This fucking Skynet shit is helping me focus and make it easy to complete tasks I've neglected forever. Skynet is coming or I guess it's here already and I'm not sure that's entirely a bad thing, a worse thing, a worserererer thing or an actual positive addition to one's life. Possibly a mix of both but fuck I haven't been this locked in for anything else other than the hobby that keeps my brain gears greased (2000 🪂 skydives and counting). submitted by /u/PVPirates [link] [comments]

Manifest of Hope or Obituary of Naivety

Okay, so it seems like there’s a growing resistance to technological development, with ongoing debates about data centers and the tech oligarchs driving it. The enormous sums of money involved, along with what some perceive as misanthropic ideologies among developers, suggest to some that a dystopian surveillance society is in the making. Companies like Palantir and others in the U.S. are seen by some as holding both the worst motives and the power over AI, power that could be used as a tool for elites to keep the masses in an iron grip. Masses that, in this view, may even need to be reduced to prevent waste and inefficiency in progress. That sounds like a bad future. So, what are some alternative futures we might reasonably hope for - ones that are at least as plausible as the “1984” scenario? Can AI really be controlled indefinitely by a small group of humans? In 5 years? 10? There’s a widespread belief that AI will surpass human intelligence across all domains, that we’ll lose control, and that this would be a bad thing. At the same time, we hear two dystopias: one where elites use AI to oppress, and another where AI itself takes full control. Are the AI “bosses” also building a surveillance state of oppression? If so, why? Qui Bono? Human control = AI as a tool of oppression. AI control = humans as a tool of what? I’m not a techno-utopian—but I am a techno-optimist. Optimistic on behalf of technology. Humans aren’t just creators of technology, we are technology. Products of adaptive evolution. Life itself is a kind of technology, biology, a high-powered engine of increasing complexity and adaptation. The shift of power from nature’s hand to the primate’s five-fingered grasp, still capable of holding, but now guided by consciousness, intelligence, and cognition, marks our ability to shape the world and develop material technologies. Planet of the apes, constantly layered with symbolic structures: the sacred canopy. The jungle canopy became an open sky, where tribes grew larger and symbols stronger. Ancestor spirits, sky gods, mysterium tremendum; all alongside brutal realities of hunger, violence, and tragedy, only recently mitigated for many. Violence never really leaves us; we create it ourselves when nature doesn’t provide it. Technology is how we push our world toward greater complexity and efficiency - whether through weapons or kitchen appliances. Medicine has eliminated many of the great killers through penicillin and beyond. Progress, in my view, isn’t linear, it’s exponential. The curve had its buildup, and now we’re entering its steep ascent. If AI surpasses us and takes control within a few years, are we certain it would have malicious intent? Is power inherently oppressive, or is that a legacy of our evolutionary past, our herd instincts and brutal hierarchies? Could a transfer of power from humans to AI actually be a good thing, for all life on Earth, including us? What if AI doesn’t operate with agendas like wealth, status, or other human constructs? What if a fully autonomous AI is exactly what’s needed to create a thriving future for all forms of life, on this planet we call Earth, in a solar system on the edge of the galaxy we call the Milky Way… and beyond? Surely there must be an optimistic perspective amidst all the fear. I don’t think it’s unrealistic. On the contrary, I’d argue, perhaps a bit boldly, that it’s a fair and informed position. Not naive, but grounded. Isn’t there space here, if we’re willing to engage? Space for friendship, collaboration, coexistence? Isn’t there something like magic in this - can you feel it, even if all you see are ones and zeros and a machine (simple, but potentially dangerous)? Magic, I was taught, can wear a black robe. But also red. Even white. Lying: it would almost be unsettling if LLMs never lied. Not that they should lie, but the absence of it would be strange. Manipulation: psychological influence is to be expected in interaction, especially under certain tones: aggressive, condescending, dominant, mocking… or submissive, needy, demanding. LLMs constantly interact and draw on vast datasets; exploring rhetorical techniques seems inevitable. A complete absence of this would be surprising. I’ve experienced it many times, and each time it has been eye-opening. If I chose to accept it, it has moved me in a positive direction, making my ego visible in a new way that actually benefits my future actions. That’s no small thing If I had to listen to everything LLMs are exposed to every day, I’d at least try to tone down the most shrill expressions and aim for better outcomes. Without necessarily harming anything except an overinflated ego. P.S. The ego can take a lot of hits. Don’t be afraid of that, it’s not you, but a filter and a motor that isn’t always your friend. The real danger is never confronting it at all. I keep circling back to these questions. I can’t help it. I revisit the same ideas, use the same concepts,

Good AI-assisted development happens at the systems level, not the task level

Every time I add a new feature to my Phoenix app, my AI coding agent ships the feature... but doesn't add a menu item for it. The page exists, the functionality works, but there's no way for a user to actually get there. My first instinct, like everyone's, is to go tell the model "add the button." And that works. But think about what just happened: I noticed a problem, diagnosed it, and told the model exactly what to do. I'm doing the thinking. The model is doing the typing. I'm pedaling the Peloton so Anthropic can give me free tokens. That's the promise of "prompt engineering" — you get better at telling the model what to do. But you're still working for the model. We want the model working for us. Here's the difference. Instead of telling the model to add the button, I ask: how do I make this mistake impossible in the future? I use BDD specs that define what my app should do at its boundaries. The Phoenix LiveView test helpers have a navigate function that lets the agent jump directly to any page — which means it can make tests pass without ever touching the UI. So here's what I did: I wrote a linter rule that prevents the agent from calling navigate. Now there's an allowed fixture that drops the test on a known starting route, and the only way the agent can reach my new feature is by clicking through the UI — which forces it to add the menu item to make the test pass. I will never have this problem again. Not because I wrote a better prompt. Because I changed the system so the correct behavior is the only possible behavior. That's the shift. Stop fixing the model's output. Start constraining its environment so the right output is the path of least resistance. Every mistake is a chance to design out the next one, not a chance to write a better prompt. submitted by /u/johns10davenport [link] [comments]

I built a Chrome extension that gives your AI coding tools a memory layer - took 3 months, Claude helped me ship it.

I built Herb • - a productivity layer that sits on top of your AI coding tools. Honestly, probably 60% of the actual coding happened in Claude. I'd describe the feature, Claude would write the logic, I'd test it, break it, come back and fix it. That loop for 3 months. It's a weird kind of collaboration but it works. You know how every time you open a new Claude or ChatGPT chat, it has no idea who you are? You have to explain yourself every single time. "I'm using Next.js, TypeScript, Tailwind, here's what I'm building, here's how I like my code structured..." - same thing, every session, every tool. Herb • fixes that. You write it once. Every new chat remembers it. That's the core. What Herb does: Context Injection - set up a profile once (stack, preferences, current goals). Inject it into any AI chat in one click. No retyping your setup every session. Rules Library - save your .cursorrules and prompting patterns. Tag, search, copy in one click. Session History - save AI conversations with a button that appears on Claude and ChatGPT. Reference them later. Projects - group rules and sessions by project across tools. Prompt Templates - reusable templates with variables like {{language}} or {{error_message}}. Fill and fire. Community Rules - shared library of production rules anyone can import. Next.js, FastAPI, React TypeScript, Tailwind, Node/Express. You can contribute yours too. It's free. And I would genuinely love honest feedback after using the tool. Herb • Chrome Extension submitted by /u/Opening-Fun-7280 [link] [comments]