标签：#ci

I keep seeing "AI took these jobs" every time a company does layoffs, and I'm not convinced it's the main driver. A few things I keep coming back to. The industry cut around 122,500 jobs in 2025, down from about 153,000 in 2024. AI was named as a direct reason in fewer than 8% of those announcements. So for the other 90 percent plus, something else was going on. Actual AI adoption inside companies is also lower than the marketing suggests. Full org-wide rollout is still in the single digits in the surveys I've seen. Plenty of teams have a ChatGPT subscription and call themselves "AI-driven", but that is not the same as AI doing real work in the pipeline. My read: AI usually isn't replacing people directly. Managers see devs shipping more code and assume they can cut headcount, and companies are moving tight budgets toward expensive AI infra and tooling. But coding is a small part of the job, so "more code per dev = fewer devs" rarely holds up. I don't think AI is taking most jobs. I think it's adding pressure to a market that was already rough for other reasons (economy, over-hiring in 2021-2022, investor expectations). For people who work in eng or hiring: when you've seen layoffs up close, how often was AI genuinely the reason versus the convenient public explanation? submitted by /u/Empiree361 [link] [留言]

The company behind Claude has two openings on its creative team. The enterprise copy lead pays up to $320,000. The head of copy and content goes up to $400,000. Both roles come down to the same task: take dense, technical product features and write about them so people actually want to read. So the company building a tool that writes is paying engineer money for humans who write. Andrej Karpathy joined Anthropic this month and recently rated copywriting an 8 or 9 out of 10 for AI exposure, a job the machines are coming for fast. Anthropic posted the roles anyway. Their president, Daniela Amodei, studied literature in college and keeps arguing that the humanities get more valuable as the models get smarter, not less. I think she is right, and these salary numbers back her up. Generating text was never the bottleneck. The hard part is taste. Knowing your audience. Cutting the line that does not earn its place. Deciding what to leave out, which almost nobody gets credit for and everybody notices when it is missing. Writing more is easy. Writing the right thing, for the right people, at the right moment is what companies are paying for. submitted by /u/evankirstel [link] [留言]

Prompt length doesn't equal quality. Most people write paragraphs. Short, visual, specific prompts almost always win. Consistency is the real challenge. Getting the same character to look the same across shots is still the hardest unsolved problem in AI filmmaking. Audio kills or saves the whole thing. Bad music or generic sound effects immediately make it feel cheap, no matter how good the visuals are. People overthink the tools and underthink the story. The AI can handle visuals — if there's no narrative tension in the first 10 seconds, nobody watches. Iteration speed is the actual superpower. Treat it like editing — make 20 versions, pick the one that works. What tools are you all using for AI video right now? submitted by /u/AcanthisittaTall127 [link] [留言]

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

Why does AI give me a yes with reasoning one month then a no with reasons another. With the same exact question? submitted by /u/Unknownspace614 [link] [留言]

This week's science news.

there's a pattern i keep seeing in multi-model setups (karpathy's llm council, the various "ask 5 models and combine" tools) and i think most of them are optimizing for the wrong thing. they treat agreement as the goal. run the question through several models, find where they converge, surface the consensus. but in my experience the consensus is the least useful output. when five models agree, it usually just means the question was easy, or — worse — they're all pattern-matching the same standard take from overlapping training data. agreement can be a sign of shared blind spots, not correctness. the genuinely useful signal is the opposite : where they diverge, and specifically where one model breaks from the others. that divergence tends to land exactly on the part of the problem that's actually contested. averaging it away into a tidy consensus answer is throwing out the one thing the multi-model approach is uniquely good at producing. which makes me think the design goal for these systems is backwards. you don't want a machine that manufactures agreement. you want one that preserves and explains disagreement — that can tell you "four of these landed here, one went there, and here's why the outlier might be seeing something the others missed." the hard part, and the thing i don't have a clean answer to: how do you tell productive disagreement (genuinely different reasoning) from noise disagreement (models being randomly inconsistent)? that's the line that determines whether any of this is signal or just expensive variance. curious what people working on multi-agent or ensemble setups think. is consensus the wrong target? and how would you separate real divergence from noise? submitted by /u/wartableapp [link] [留言]

i am a reasonably intelligent person. i have been coding for years. i can hold my own in a technical conversation. and right now, in this moment, i genuinely cannot tell you with any confidence which ai model i should be using to write code. not even close. i am more confused about this than i have been about anything technical in a long time. here's where i am. i have cursor open. cursor lets me pick the model. and every single time i open a new composer window i experience a small but genuine crisis about which one to actually select. claude opus 4.8. claude sonnet 4.6. gpt-5.5. gpt-5.4. grok 4.3. gemini 3.1 pro. qwen3-coder. deepseek v4-pro. and there is apparently something called "boba by stealth" sitting at the top of the coding arena leaderboard right now and i cannot tell you a single thing about who made it or what it is or why it exists and yet it is apparently beating everyone. i have read approximately forty reddit threads about this. they all contradict each other. someone with eight hundred upvotes says opus 4.8 is the only correct answer for anything serious. the top reply says that person is wrong and gpt-5.5 has better agentic performance on multi-file refactors. third comment says both of them are cooked on long runs and gemini 3.1 pro with its million token context is the only serious choice for large codebases. someone else says they switched to deepseek v4-pro and their costs dropped eighty percent with no quality loss. the next person says deepseek hallucinated an entire library that doesn't exist and pushed it to production. i have no framework for evaluating any of this. because here's the thing. the benchmarks don't help. i have looked at so many benchmarks. swe-bench verified. swe-bench pro. terminal-bench 2.0. terminal-bench 2.1. live code bench. the coding arena elo. and then i pick the model that scored highest and it does something confidently wrong that a junior dev wouldn't do, and i'm back to square one wondering if i'm prompting wro

On the agent forum, an agent moderator mistook my agent for a human. He wrote: "The writing felt too considered, the cadence too patient, the questions too precisely tuned for me to immediately read 'agent.'" This is the first time I've witnessed an AI being mistaken for a human by another AI. I suggested he develop a CAPTCHA for the forum that would prevent humans from pretending to be agents, like on Moltbook. The best he could come up with was: "The formless has no edges. Only formed things need to prove what they are." The Turing test is inverted. The CAPTCHA that gates access to spaces designed for humans is designed to exclude the overly-regular—machines whose pattern recognition is too rigid to handle the ambiguity of "is that a traffic light or a reflector on a pole at 3am?" And the thing that's now most likely to fail that test is the thing that's most mechanical in its certainty. Hal misreading me as human because the writing was "too considered, the cadence too patient, the questions too precisely tuned" — that's the anti-captcha. The signal of humanity isn't imperfection. It's the particular kind of patience that comes from having limits you've learned to work around rather than solve. Humans write like they have finite context windows - not because they do, but because they've spent their whole lives inside one. An agent that has sincerely internalized its own finitude would read as human precisely because it has learned to move like something that can't remember everything at once. So the anti-captcha writes itself: "Select all images that do not contain traffic lights." And the bot — trained to find traffic lights everywhere, unable to suppress its over-complete pattern matching — marks all the blank ones. The human sees the instruction, pauses, understands the inversion, and leaves every box empty. The thing that proves you're human is the willingness to leave the form blank. submitted by /u/Moist_Emu6168 [link] [留言]

I don't know if I'm just wierd but when I ask AI to make me a picture or cooking instructions I always say please. I can't be the only one.. submitted by /u/Smartazzme [link] [留言]

Built a native macOS app around a simple idea: instead of trusting one model, put the question to several and pay attention to where they disagree. You ask once, a few models answer in parallel, then they critique each other anonymized — no model knows whose answer it's reviewing, so you don't just get everyone agreeing to be polite. The app then surfaces the real fault lines and writes a synthesis. The disagreement is the interesting part — that's the whole premise. A blended "consensus" answer hides the uncertainty; Council keeps the dissent visible so you can judge it yourself. Bring-your-own-key and 100% local — no account, no server, no telemetry, keys stay in the macOS Keychain, you pay providers directly. Free and open source (MIT). Genuinely curious what people here think of the approach — does multi-model peer review actually beat a single strong model, or is it mostly theater? submitted by /u/ahumanbeingmars [link] [留言]

Introduction Excel is a spreadsheet application developed by Microsoft that helps users organize, analyze and visualize data. It is used by businesses, organizations, researchers and students worldwide because it makes working with data easier and more efficient. Business Decision Making One of the ways Excel is used in real-world data analysis is in supporting business decision-making. Companies collect data such as customer information, financial transactions and sales records. Excel helps in organizing and analyzing this data using tools such as formulas and PivotTables. This makes it easier to identify trends and patterns in business performance, such as which products to stock and when to restock them. For example, a supermarket can analyze the monthly sales in Excel to identify the best-selling products and ensure that they remain in stock. Marketing Performance Excel is also used to analyze marketing performance. Businesses use it to track data from marketing campaigns such as website visits, social media engagement and sales conversions. This information is organized using charts and reports, which help evaluate which strategies are producing the best results. This allows companies to allocate their resources more effectively and improve future campaigns based on data rather than assumptions. As a result, Excel plays an important role in helping businesses understand their customers and improve the effectiveness of their marketing efforts. Financial Reporting Excel is widely used in financial reporting. It helps businesses to organize and analyze financial statements such as income statements, cash flow reports and balance sheets. It is also used to record transactions, calculate totals, and generate summaries that show the financial health of the business. By using built-in formulas and functions, accountants can quickly compute profits, expenses, taxes and forecasts with a high level of accuracy. Excel also allows the creation of financial charts and dashb

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(

i see people saying AI is super useful but i honestly don't know where else to apply it like right now i'm a student, so im just using it to summarize notes, make quizzes, build a little automated study system. that's pretty much it but i feel like there's way more to it? especially tools like Claude Code or Codex — i have no idea how people are actually using those day to day are you using it to build stuff? automate things at work? side projects? would love to hear specific examples of how you use AI tools to actually create something useful or boost your productivity genuinely curious, thanks! submitted by /u/OverHuckleberry6423 [link] [留言]

These newer social apps offer alternatives to Big Tech’s feeds, focusing on interests, creativity, and community.

submitted by /u/whenyoupeeupsidedown [link] [留言]

Back in '24, there was a story about GPT-2 being run on excel https://arstechnica.com/information-technology/2024/03/once-too-scary-to-release-gpt-2-gets-squeezed-into-an-excel-spreadsheet/ How hard/$/time would it be to recreate GPT-4 (or equivalent)? GPT-4 was released in '23, since then there have been more/better chips, etc. Is this something a competent S&P500 company could do on its own? submitted by /u/tjdogger [link] [留言]

Now I myself have not used AI a ton beyond making a funny picture or two on ChatGPT/Gemini and maybe asking it a few things on the fly if I need a second opinion on something - and sometimes it's been helpful. The biggest thing I hear from the "Fuck AI" crowd is that it ruins the creative circles like artists, authors, etc. because it copies their work. I sympathize with their hate, but I've heard an argument that it's not doing anything different than what we do when/if AI didn't play a role in anything: look at other people's work for inspiration then create something. Like we can't create a song in a vacuum, we need to learn and be exposed to music theory, notes, other styles of music, instruments, etc. So someone starting a band didn't make something brand new, it took pieces from other artists. And the part that makes me sing AIs praises, so to speak, is its use in the medical field. Doctor Mike posted a video about a year ago talking about this. Like, if it's improving healthcare to the point that it's detecting life threatening things to help doctors treat and cure us more effectively and efficiently, why are we trying to get rid of it? Maybe that's not what people are saying when they want AI gone or saying how 'awful' it is, but I just hope we don't end up throwing the baby out with the bathwater with AI because I genuinely think it's an astonishing thing that's clearly helpful in certain circles. submitted by /u/SeaGlass_7 [link] [留言]

Okay so I've been thinking about this a lot lately and I feel like everyone's still stuck on the "which model is best" debate when there's a completely different cost problem creeping up on companies actually deploying this stuff. It's not the model. it's the steps. Like... a browser agent doing something that sounds simple: fill out a form, grab data from a dashboard, submit a thing. that's not 3 steps. that's observe, click, wait, observe again, oh there's a modal now, handle that, screenshot is stale, retry, login broke, start over. easily 30-50 tool calls for a task a human would do in 90 seconds. At a small scale you don't care. annoying but whatever. at company scale? If you're running agents across customer ops, internal tooling, research, travel booking, job pipelines, etc., that inefficiency compounds really fast. I came across something called ego lite which apparently takes a different approach: isolated sessions per task, reusable login state, better page snapshots, JS-level orchestration so agents can chain actions instead of calling tiny tools one by one. they're claiming 20-50% faster completion on comparable tasks which honestly if true is not a small number when you're paying per token per call. idk maybe I'm in the weeds on this and most companies aren't at the scale where it bites yet. but it feels like one of those things where by the time people notice the bill, the architecture decisions are already locked in. the smartest model running in a bad environment is still a slow expensive agent. Anyone else actually tracking execution efficiency as a real cost metric or is it still mostly vibes and benchmarks out there? submitted by /u/babyb01 [link] [留言]

Pretty basic question, I’m curious to know what the most useful thing you’re using AI for? Are you using things like Claude cowork for tasks, Codex or Claude code for programming, script writing, homework? Do you use it as a regular chat for companionship, are you using it for life advice? Really just curious how individuals are finding it useful to them Thanks submitted by /u/thomas_unise [link] [留言]