daily

2026-07-13
1

Six people rescued at Rosslare Strand

Wexford Local · original → · 8/10 · Local Wexford news: rescue incident at Rosslare Strand
[image →] By Dan Walsh Six people were rescued from the water at Rosslare Strand on Sunday afternoon following an incident where a young swimmer was in difficulty in the water and brave rescuers…

By Dan Walsh

Six people were rescued from the water at Rosslare Strand on Sunday afternoon following an incident where a young swimmer was in difficulty in the water and brave rescuers ended up in difficulty themselves.

All of those brought to hospital are currently believed to be in a stable condition.

Operations Resource Manager with the National Ambulance Service Ger Carthy confirmed that the National Ambulance Service responded to a significant incident at Rosslare.

“They transferred five people by land ambulance and our colleagues from Rescue Helicopter 117 airlifted one person to a waiting ambulance at Wexford Wanderers Rugby Club for transfer to Wexford General Hospital which is close-by.

“This multiple agency response again reassured the public of the capability of our emergency services in a time of grave need,” said Mr Carthy, who also praised “the fast actions of An Garda Síochana, the Coast Guard and the general public during the emergency incident.”

This was a rapid emergency response with the Coast Guard Rescue 117 helicopter, Rosslare and Curracloe Coast Guard Units, Rosslare Harbour Lifeboat, Gardaí and the National Ambulance Service (NAS) all in attendance.

2

Russia sanctions are ‘priority’ for Irish presidency of EU – Helen McEntee

Breaking News Ireland · original → · 7/10 · Irish affairs: EU presidency priorities affecting citizens broadly
Further sanctions against Russia are a “major foreign policy priority” for the Irish presidency of the EU, Foreign Affairs Minister Helen McEntee has said. The minister is in Brussels to attend a…

Further sanctions against Russia are a “major foreign policy priority” for the Irish presidency of the EU, Foreign Affairs Minister Helen McEntee has said. The minister is in Brussels to attend a meeting of EU foreign ministers, the first meeting of the Foreign Affairs Council (FAC) under Ireland’s EU presidency. Ministers will discuss a range of issues, including the situations in Ukraine and the Middle East. They will also have an exchange with the foreign ministers of the Gulf Cooperation Council (GCC) during the third EU-GCC High-Level Forum on Regional Security and Cooperation. Ms McEntee will also co-host an event with the EU High Representative for Foreign Affairs and Security Policy, Kaja Kallas, which will focus on Ukrainian detainees in the occupied territories in Ukraine. She will also attend a meeting of the Palestine Donor Group. Ms McEntee said: “I look forward to working closely with High Representative Kallas and our EU partners to secure progress across all the priorities we have set for the council over the next six months. “Ensuring continued support for Ukraine will be central to our work for the next six months. “We were honoured to host President Zelensky in Dublin on the first day of our EU presidency. “It was also important that we begin our term with an event that highlights a key issue for Ukraine and the Ukrainian people. “During my visit to Ukraine in March, I saw first-hand the terrible crimes inflicted upon innocent civilians by Russian forces at Bucha.” She added: “It is essential that we maintain our support for Ukraine and keep pressure on Russia. “As we have seen with Russia’s continued indiscriminate missile and drone attacks on Kyiv and other Ukrainian cities, Moscow continues to show no interest in peace. “It is clear that our sanctions are having an impact. “I have been clear in saying that further sanctions against Russia are a major foreign policy priority for the Irish presidency.” The minister said a “significant focus” of the meetings in Brussels would be the Middle East. She said: “The recent exchange of fire between Iran and the US risks undermining the agreement on a ceasefire and of the ongoing talks required to address the issues that remain in the region. “We will meet with our Gulf counterparts to discuss how the European Union can assist efforts to de-escalate the situation which, as we all know, has a direct financial impact on European citizens. “Iran’s latest attacks on Kuwait, Bahrain and Jordan are unacceptable, as are its attacks on commercial shipping in the Strait of Hormuz. “Freedom of navigation must be upheld, in accordance with international law. As I emphasised during my recent visit to the Gulf, only the hard work of dialogue and diplomacy will bring lasting stability and security to the region.” Ms McEntee said: “We will also discuss the catastrophic situation in Gaza and the West Bank. Conditions continue to deteriorate. “With my counterparts on the council, I will seek to build consensus around a position that addresses the consistent violations of human rights and international law by the current Israeli government, which undermine the viability of the two-state solution. “This will include trying to reach an agreement at the EU level to address trade with illegal settlements.”

3

Tiny Emulators

Hacker News · original → · 7/10 · Retro gaming: emulators for classic systems
Tiny Emus HALP! github twitter blog Visual 6502 Remix Visual Z80 Remix KC85/2 UI KC85/3 UI KC85/4 UI KC Compact UI Amstrad CPC464 UI Amstrad CPC6128 UI ZX Spectrum 48k UI ZX Spectrum 128 UI…

Tiny Emus HALP! github twitter blog Visual 6502 Remix Visual Z80 Remix KC85/2 UI KC85/3 UI KC85/4 UI KC Compact UI Amstrad CPC464 UI Amstrad CPC6128 UI ZX Spectrum 48k UI ZX Spectrum 128 UI Commodore VIC-20 UI Commodore C64 UI Acorn Atom UI LC-80 Robotron Z1013 UI Robotron Z9001 UI Robotron KC87 UI FORTH (KC85/4) UI FORTH (Z1013) UI BASIC (Z1013) UI ASMDEV (KC85/4) UI CP/M 2.2 (CPC) UI DTC (CPC) UI Tire Au Flan (CPC) UI Wolfenstrad (CPC) UI Byte'98 (CPC) UI Ecole Buissonniere UI Demoizart (CPC) UI Logon's Run (CPC) UI YAP! (CPC) UI Backtro (CPC) UI Isometrikum (CPC) UI SotB Demo (CPC) UI Points Barres (CPC) UI Still Rising (CPC) UI Octopus Pocus (CPC) UI Gloire a Piou! (CPC) UI phX / Condense (CPC) UI Batman Forever (CPC) UI CRTC (CPC) UI Wunderbar (CPC) UI Phortem (CPC) UI Pheelone (CPC) UI Phreaks (CPC) UI Phat 2 (CPC) UI Wobbler (CPC) UI Battro (CPC) UI Rebels 1989 (C64) UI $777 (C64) UI The Wobbler (C64) UI Dawnfall (C64) UI +H4K (C64) UI Crapman (C64) UI Kongo2 (C64) UI C.Intentional (C64) UI Sour & Salty (C64) UI Nightcrawler (C64) UI Arcade Intro (C64) UI In A Loop 1K (C64) UI Dubious 4K (C64) UI Space Invaders (C64) UI Rewind (C64) UI Tunnel Vision (C64) UI Party Horse (C64) UI Funny Rasters (C64) UI Field Sort (C64) UI One Bit Wonder (C64) UI Swirl (C64) UI The Earth (C64) UI F600 (C64) UI Reflections (C64) UI Summer of 64 (C64) UI One-Der (C64) UI PPY (VIC20) UI Muna.Paluu (VIC20) UI Serious (KC85/4) UI Twenty (KC85/4) UI Ancient Civilizations (KC85/4) UI Stereo (KC85/4) UI Fractal Dimension (KC85/4) UI Digger (KC85/3) UI Jungle (KC85/3) UI Pengo (KC85/3) UI House (KC85/3) UI Cave (KC85/3) UI Cave (KC85/3) UI Labyrinth (KC85/3) UI Pacman (KC85/3) UI Ladder (KC85/3) UI Chess (KC85/4) UI Enterprise (KC85/4) UI Mad Breakin (KC85/3) UI Tetris (KC85/4) UI Sokoban (KC85/4) UI Boulderdash (C64) UI Boulderdash (CPC) UI Boulderdash (ZX48k) UI Boulderdash (KC85/3) UI Boulderdash (KC85/4) UI Boulderdash (Z1013) UI Bomb Jack (CPC) UI Bomb Jack (ZX48k) UI Bomb Jack (Arcade) UI Pengo (Arcade) UI Zaxxon (C64) UI Dig Dug (C64) UI Cybernoid (CPC) UI Cybernoid (C64) UI Arkanoid (CPC) UI Arkanoid RoD (CPC) UI Arkanoid RoD (ZX128) UI Batty (ZX48K) UI Breakout (KC85/3) UI Silkworm (ZX128) UI Silkworm (CPC) UI Great Escape (ZX128) UI Flying Shark (ZX48K) UI Chase HQ (CPC) UI Chase HQ (ZX128) UI Ghosts'n'Goblins (CPC) UI Ghosts'n'Goblins (C64) UI Fruity Frank (CPC) UI 1943 (CPC) UI Dragon Ninja (CPC) UI Gryzor (CPC) UI Astro Marine Corps (CPC) UI Bruce Lee (CPC) UI Bruce Lee (ZX48K) UI Intern.Karate (CPC) UI IK+(CPC) UI Ikari Warriors (CPC) UI Head over Heels (CPC) UI Head over Heels (ZX) UI Rick Dangerous (CPC) UI Prince of Persia (CPC) UI Nebulus (CPC) UI Nebulus (C64) UI Live and Let Die (CPC) UI Cyclone (ZX48k) UI Exolon (ZX48k) UI Quazatron (ZX128) UI Rainbow Islands (ZX) UI Sir Fred (ZX48K) UI Chucky Egg (Atom) UI Jet Set Willy (Atom) UI Alien Blitz (VIC-20) UI Mazogs (Z1013) UI Galactica (Z1013) UI Demolation (Z1013) UI

4

Guy took Jupiter photo with Game Boy Camera, giant telescope, publishes tutorial

Hacker News · original → · 7/10 · Retro gaming: Game Boy Camera Jupiter photography tutorial
Guy who took photo of Jupiter with a Game Boy Camera and giant telescope publishes DIY tutorial The latest adventure for the kooky camera looks skyward. Musician and retro tech fan Chris Graue made…

Guy who took photo of Jupiter with a Game Boy Camera and giant telescope publishes DIY tutorial The latest adventure for the kooky camera looks skyward. Musician and retro tech fan Chris Graue made headlines last month after using a Game Boy Camera to take a photo of Jupiter. Obviously, a Game Boy Camera isn't capable of that kind of range on its own. He and some colleagues connected the novelty camera to the eyepiece of the Hooker Telescope at the Mount Wilson Observatory thanks to a 3D-printed adapter. Now, Graue has released the schematics for the adapter, meaning anyone can print their own for free. He's also posted a quick tutorial video about it. Remember my Game Boy Camera Telescope I took a picture of Jupiter with? Now you can too. I'm making the 3d printable lens adapter available for free youtube.com/shorts/irHY8... — Chris Graue {Lo(u)ser} (@chrisgraue.com) 2026-07-08T18:21:33.018Z In his own words, the adapter is "a tube that pressure fits inside of a standard 1.25 inch eyepiece for telescopes." So you may not have access to a telescope powerful enough to see Jupiter, but you can still use it to take some pretty nifty shots with the Game Boy Camera. If space isn't your jam, there are plenty of other creative things modders and DIYers have done with their Game Boy cameras over the years. We've seen them become mirrorless cameras, webcams and telephoto lenses.

5

Claude Code sends 33k tokens before reading the prompt; OpenCode sends 7k

Hacker News · original → · 7/10 · AI: token efficiency analysis of Claude vs OpenAI
Claude Code Is Way More Token-Hungry Than OpenCode. We Measured Exactly How Much We put Claude Code and OpenCode on the same model, the same machine, and the same tasks, then examined everything…

Claude Code Is Way More Token-Hungry Than OpenCode. We Measured Exactly How Much We put Claude Code and OpenCode on the same model, the same machine, and the same tasks, then examined everything sent and received. Claude Code is far hungrier: When we asked both harnesses for a one-line reply, Claude Code used roughly 33,000 tokens of system prompt, tool schemas, and injected scaffolding before the prompt even arrived. OpenCode used about 7,000. That first test was on Sonnet 4.5. Re-running on Claude Fable 5 narrowed the gap to about 3.3x, because Claude Code sends newer models a much smaller system prompt; still far hungrier, but the multiple is model-dependent. Claude Code is far more cache inefficient: OpenCode's request prefix was byte-identical in every run we captured; it paid to cache its payload once per session and read it back for pennies. Claude Code on the other hand re-wrote tens of thousands of prompt-cache tokens mid-session, run after run, and on the same task wrote up to 54x more cache tokens than OpenCode. Cache writes of course are billed at a premium, which accounted for the usage dashboard climbing when using Claude Code. Config further bloats the prompt: A production repository's 72KB instruction (AGENTS.md or CLAUDE.md) file adds another (avg) 20,000 tokens to every single request. Five modest MCP servers add 5,000 to 7,000 more. By the time a real working setup sends its first request, it is 75,000 to 85,000 tokens deep before the user has typed a word. Subagents add to the cost: A small task that cost 121,000 tokens done directly cost 513,000 tokens when fanned out to two subagents, because every subagent has its own bootstrap cost, and the parent then consumes its transcript. We found one result in favour of Claude Code: On a multi-step task Claude Code's whole-task total came out lower than OpenCode's, because it batches tool calls into fewer requests while OpenCode re-pays its smaller baseline turn after turn. The meter starts higher; how the session unfolds decides who spends more. The rest of this post shows how we measured all of this at the API boundary, where the tokens go, and what prompt caching does and does not save you. Why measure this at all Token overhead is cost, latency, and context budget. Every token of harness payload is a token of working context you cannot spend on code, and the baseline is re-sent, or re-read from cache, on every single turn. If you operate agentic AI in production, particularly under the EU AI Act where Article 12 expects you to log and understand your system's behaviour, "what does my agent actually send" is a question you should be able to answer with data rather than folklore. Method We spliced a logging proxy between each harness and the model endpoint. harness (Claude Code / OpenCode) → logging proxy (captures request payloads + response usage) → model endpoint The proxy records two things per request. The first is the exact JSON payload the harness emitted, meaning the system blocks, tool schemas, and messages. The second is the usage block the API returned, covering input tokens, cache writes, cache reads, and output tokens. The payload capture is ground truth for what the harness sends. The usage block is ground truth for what was metered. We tested under these conditions. - Harnesses. Claude Code 2.1.207 and OpenCode 1.17.18, both pinned to claude-sonnet-4-5 , July 2026. A reduced matrix (the floor, the cache task, and the multi-step task) was later re-run pinned to claude-fable-5; where the model changed the result, we say so inline. - Baseline isolation. Fresh config directories with no MCP servers, no user settings, and no memory; an empty workspace with no instruction files; permissions bypassed. Multiplier lanes then add one variable at a time. - Tasks. T1 says "Reply with exactly: OK" and isolates fixed overhead (three runs per harness). T2 reads a seeded file and summarises it. T3 is a write-run-test-fix loop against FizzBuzz plus a checker script. - Zero-tools variant. Claude Code with --tools "" and OpenCode with"tools": {"*": false} , separating system prompt from tool schema weight. One honesty note before the numbers. Our traffic passes through a local LLM gateway that wraps requests in its own envelope, a constant we measured at roughly 6,200 tokens with bare calibration requests and subtracted from every metered figure below. Payload-level figures come from the captured request bodies, which the gateway cannot affect, and are exact. Character-to-token conversion for component estimates uses each harness's own measured ratio of 4.1 to 4.4 characters per token, derived from cold-cache anchors where the metered write equals the full payload, rather than a generic heuristic. Part I. The floor The fixed overhead of saying OK The task was 22 characters. Here is what each harness sent with it on its first request. Component | Claude Code | OpenCode | |---|---|---| System prompt | 27,344 chars, 3 blocks | 9,324 chars, 1 block | Tool schemas | 27 tools, 99,778 chars | 10 tools, 20,856 chars | First-message scaffolding | 7,997 chars of | none | The actual prompt | 22 chars | 22 chars | First-turn payload (calibrated) | ~32,800 tokens | ~6,900 tokens | OpenCode's request is close to minimal. There is one system block that opens with "You are OpenCode, the best coding agent on the planet", ten classic coding tools, and your prompt as the only user content. Claude Code's request is a platform bootstrap. The 27 tools include the coding core plus an entire background-agent and orchestration suite, from CronCreate and Monitor to the Task family, worktree management, and push notifications. Before your prompt, its first user message carries three injected reminder blocks; a catalogue of agent types for delegation, a catalogue of available skills, and user context. Tool schemas are the dominant term for both. Roughly 24,000 of Claude Code's ~33,000 tokens are tool definitions, versus roughly 4,800 of OpenCode's ~6,900. Zero tools, pure harness Stripping the tools isolates the system prompt itself. Claude Code's weighs in at 26,891 chars, about 6.5k tokens. OpenCode's is 8,811 chars, about 2.0k tokens. Both harnesses trim their prompt slightly when tools are disabled. Even with no tools at all, Claude Code's instruction set is over three times the size of OpenCode's; the residual is behavioural doctrine, meaning tone rules, safety guidance, task-management instructions, and environment description. A one-tool task T2 asked each harness to read a file and summarise it. Both produced correct summaries. Claude Code took 6 HTTP requests and roughly 199,000 cumulative metered input tokens. OpenCode took 4 requests and roughly 41,000, plus one Haiku side call for session titling. Most of those tokens are cache reads billed at a tenth of the input price. Three things scale with payload regardless; the first-turn cache write, the per-turn read, and context-window consumption, which no cache discount reduces. A 33k-token baseline means every turn starts a sixth of the way into a 200k window before any code enters the conversation. A multi-step task, where the gap closes T3, the write-run-test-fix loop, inverted the expectation set by the baselines. Metric | Claude Code | OpenCode | |---|---|---| Model requests | 3 | 9 (+1 title call) | Tool-calling style | parallel batch in one round trip | one tool call per turn | Cumulative metered input | ~121,000 tokens | ~132,000 tokens | Claude Code batched the entire job, two file writes and two script executions, into a single parallel tool round trip. OpenCode made exactly one tool call per turn and took nine. Because the baseline is re-sent on every request, request count multiplies baseline. OpenCode paid its ~7k baseline nine times, Claude Code paid its ~33k three times, and the totals converged. Whole-task input roughly equals baseline times request count, plus conversation growth. A large-baseline harness that batches aggressively and a small-baseline harness that serialises can land in the same place. Two structural details emerged from the payloads. Claude Code injects an additional <system-reminder> block as the conversation progresses, three on the first turn and four by the first tool round trip, so its scaffolding grows with turn count. OpenCode's per-turn marginal payload, roughly 400 to 2,200 chars per turn, is pure conversation content. Does a newer model change the picture? We re-ran the floor on Claude Fable 5 to check whether the gap was a Sonnet artefact. It shrank, for a reason we did not expect. Claude Code's system prompt is model-conditional. It sent 27,787 chars of instructions to Sonnet but only 10,526 to Fable, with tool schemas also trimmed from 99,778 to 82,283 chars. Same 27 tools, much less doctrine. OpenCode's payload was byte-identical across both models. The floor gap on Fable comes out at roughly 3.3x by payload against 4.7x on Sonnet. Still far hungrier, but the ratio is model-dependent. Part II. The multipliers The floor explains a session that starts lean and stays short. Real sessions do neither. We measured each layer that real usage stacks on top. Multiplier 1. The instruction file We dropped a real 72KB AGENTS.md from a production repository into the workspace and re-ran T1. The effect is symmetrical and large. Both harnesses gained just over 20,000 tokens per request. OpenCode's metered total went from 13,152 to 33,336. Claude Code's went from 39,005 to 59,243. The asymmetry is in the mechanics, and it bit us during the experiment. Claude Code 2.1.207 ignored AGENTS.md entirely and only ingested the file when renamed CLAUDE.md , injecting it into the first user message. OpenCode reads either filename and injects it into the system prompt. Two practical consequences follow. Check which filename your harness actually honours, because an ignored instruction file is silent. And know that a heavy instruction file nearly quadruples a lean harness's baseline; it rides on every request of every session in that repository. Multiplier 2. MCP servers We attached public, credential-free MCP servers in one-server and five-server configurations. The schemas are identical across harnesses, so the tax is nearly identical too; roughly 1,000 to 1,400 tokens per small server, per request. Five servers added 4,900 tokens to Claude Code by payload and 6,967 metered to OpenCode, growing the tool counts from 27 to 69 and from 10 to 52. Small public servers are the gentle case. Production servers with rich APIs ship schemas several times larger, which is exactly what the everything measurement below shows. One operational footnote. Claude Code silently ignored a project-scoped .mcp.json in print mode until passed an explicit --mcp-config flag. If you assume a server is attached, verify it at the boundary. Multiplier 3. Framework templates Story-driven workflow frameworks such as BMAD expand a slash command into a large prompt template of personas, protocols, and checklists. We ran an 8,405-char representative template as the prompt for the same T3 story in both harnesses. The template itself is only about 2,100 tokens, but it enters the conversation history and is re-carried by every subsequent request in the session. A 9-request session re-sends it nine times. Framework tax is template size times request count, and it stacks on top of everything above. Multiplier 4. Subagents We asked each harness to fan the work out to two parallel subagents. This is where the totals climb fastest. Claude Code completed the task with 9 model requests across three distinct request classes. There was the main session with its full ~33k baseline, and five subagent calls each carrying their own bootstrap of a 3,554-char agent system prompt plus 24 of the 27 tools. Cumulative metered input reached 513,000 tokens, against 121,000 for the same work done directly. That is a 4.2x multiplier for one modest fan-out, because every subagent pays its own bootstrap and its transcript is then ingested by the parent. OpenCode's design here is notably leaner. Its subagent requests carry a reduced profile of a 1,379-char system prompt and 5 tools. Its subagent lane did not complete cleanly through our gateway, so we report the design difference from the captured payloads and leave its totals unquantified. If your heavy sessions surprise you, this is the first place to look. Delegation is powerful and sometimes correct; it is also the single largest token multiplier we measured. Multiplier 5. Extended thinking Thinking output bills at output rates, five times the input rate, and reasoning blocks are carried forward inside the conversation. We attempted to toggle extended thinking in both harnesses and are declining to publish numbers. Our gateway applies its own thinking policy, neither harness's toggle demonstrably survived the path, and anything we quoted would be noise. The mechanism is not in doubt, though. On reasoning-heavy work it compounds with every multiplier above, because the thinking blocks join the history that gets re-sent. The everything number Finally, the bridging measurement. We ran T1 again under a real working configuration. For OpenCode that meant eleven MCP servers covering email and calendar, task management, reference management, product analytics, and others, plus the 72KB instruction file. The first request metered 90,817 tokens on a cold cache write, carrying 179 tools and 277KB of schemas, before the user had typed a word. For Claude Code, four MCP servers plus installed plugins and the same instruction file produced a 311KB payload of roughly 75,000 tokens with 118 tools. After subtracting the gateway envelope, that is roughly a 12x configuration multiplier against OpenCode's ~7,000-token floor. The harness sets the floor; your configuration sets the bill. The cache economics Prompt caching changes the units but not the conclusions. Both harnesses set cache breakpoints correctly. The payload is written once, at a 1.25x premium for the 5-minute TTL, and re-read at a tenth of the price thereafter. Three costs survive the discount. First, the write itself, re-paid whenever a pause exceeds the TTL. A five-minute think, a meeting, a lunch; each re-primes the full stack at write rates. Second, the read multiplied by request count, which subagent fan-outs and serial tool loops inflate quickly. Third, context-window consumption, which is completely immune to caching. An 85k-token bootstrap occupies more than 40 per cent of a 200k window on every single request, shrinking the room for actual code before compaction kicks in and spends yet more tokens summarising. Cache stability, the decisive difference Caching only pays if the prefix stays stable, so we hashed the tools array and system blocks of every request in the dataset. OpenCode emitted byte-identical prefixes across every request and every run. Three separate T1 sessions produced the same tools bytes, the same system bytes, and the same message bytes; the repeat runs wrote zero cache tokens and read everything. Its nine-request T3 session held one stable prefix throughout. Claude Code emitted three distinct request classes per session; a warmup probe, the main conversation, and subagent calls, each with its own prefix and therefore its own cache entry. Its system bytes also varied between sessions in the same workspace, and its first-message scaffolding varied between runs. The consequence shows up in the cache-write column. On the identical file-summarise task, Claude Code wrote 53,839 cache tokens across five requests, including one complete mid-task re-write of its full ~43k prefix. OpenCode wrote 1,003. We re-ran the matched task to check whether that was a one-off. It was not. The large mid-session re-write reproduced, 43,342 tokens in the first run and 36,899 in the second, while a third run against a freshly warmed cache wrote almost nothing. OpenCode showed zero mid-session re-writes in every session we could cleanly meter. We then repeated the matched task on Claude Fable 5. The behaviour replicated almost exactly; another complete mid-session re-write, this time 50,053 tokens with zero cache read, and a cache-write gap of 52x against Sonnet's 54x. Two model families, same pattern. OpenCode's prefix stayed byte-identical on both. Depending on cache temperature, Claude Code's cache-write volume on the same task ranged from 5.9x to 54x OpenCode's, and cache writes bill at a premium, 1.25x base rate for the 5-minute tier and 2x for the 1-hour tier. One attribution caveat is owed here. A single mid-task cache miss could in principle be our gateway evicting rather than the harness moving its cache breakpoints; reproduction across runs makes systematic harness behaviour the likelier explanation, and the prefix instability itself is harness-side, visible in the captured bytes before any gateway involvement. If you have watched a usage meter climb dramatically under Claude Code but stay flat under OpenCode with the same model, this is the likeliest mechanism; bigger prefixes, more distinct prefixes per session, and more re-writes of them, multiplied by any subagent fan-out. What about quality? A fair objection to everything above is that a bill says nothing about the work. Paying more is rational if the output is better. The tasks here were chosen so that quality could not be the explanation. Both harnesses completed every scored task correctly. The multi-step task was verified by an assertion script each harness had to write and then pass, and both exited clean. The file summaries were both accurate. On these tasks the token gap is the cost difference for an identical outcome, which is exactly what makes it measurable. Whether the premium buys quality on real engineering work is a different question, and we did not measure it. Claude Code's background agents, skills, and orchestration surface may well earn their tokens on harder tasks. That claim deserves its own benchmark, with a proper test suite and enough runs to score pass rates, and the rig here can drive it. Two of the findings are independent of quality, though. Re-writing a byte-identical cache prefix mid-session buys no code quality at all; it is the same content, paid for again at premium rates. An instruction file the harness silently ignores buys nothing either. Whatever the capability argument for a larger platform, those two are waste on any definition. Dogfooding, or the benchmark dataset as an audit log The honest version of this experiment is "trust the captured payloads", so we treated the dataset the way we tell clients to treat production inference logs. Every one of the 185 captured request/response records was written into a tamper-evident, SHA-256 hash-chained audit trail using our open-source library @systima/aiact-audit-log , and the chain verifies end to end. Chain verified: 185 entries No breaks detected Hash chain integrity: VALID This is the same mechanism the library provides for EU AI Act Article 12 logging; structured records, integrity you can hand to a third party, and reconstruction of exactly what was sent and returned. A token benchmark is a low-stakes use of it. A credit-decisioning agent is not. Caveats - One machine, one pair of versions, two model families for the floor and cache lanes (Sonnet 4.5 and Fable 5), one for the multiplier lanes, small n (three T1 runs, three T2 runs, one run per multiplier lane). Harness prompts change frequently, so treat the numbers as a July 2026 snapshot and the method as the durable artefact. - A local gateway sat in the measurement path. Component-level figures come from captured payloads, which it cannot affect. Metered figures are cold-cache anchors calibrated against its measured constant; warm-run metered numbers were unattributable, so we only quote cold anchors. The gateway also silently substituted a newer model snapshot than the one we pinned, which is its own lesson. If you are not logging at the API boundary, you do not know what model you are actually running. On the Fable path the gateway also resumed a stale server-side session in one lane and executed tools host-side in another, so the Fable multi-step lane for Claude Code was excluded rather than reported. - The T3 convergence is one observation of one task shape. A strictly sequential task would push Claude Code's request count, and therefore its total, back up. The OpenCode zero-tools and subagent lanes returned malformed streams through the gateway, so for those conditions we report captured payload sizes only. - The real-configuration figures describe one practitioner's setup, reported in sizes and counts only. Yours will differ; the method transfers. Reproducing it The measurement rig is roughly 200 lines of Node. It is an HTTP proxy that forwards to your model endpoint, writes each request body and response usage block to disk, and appends each pair to an audit chain. Point ANTHROPIC_BASE_URL at it. Give the harness a fresh config directory and an empty workspace for the floor. Then add your instruction file, your MCP servers, and your workflows one at a time, and watch the boundary. If your traffic passes through a gateway, measure its envelope with a bare request first, and check which model actually answers. If you run agentic systems in production and cannot currently answer "what exactly did we send to the model last Tuesday", that is the gap worth closing first. The token accounting falls out of it for free.

6

How tech workers actually feel about AI in 2026 | Annual AI sentiment survey (Noam Segal)

Lenny's Newsletter · original → · 7/10 · AI: tech worker sentiment survey on AI in 2026
[image →]Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the…

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year’s survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.

In our in-depth conversation, we discuss:

  1. Why AI has split the tech workforce almost exactly in half—one half that’s thriving, another that’s shaken

  2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)

  3. Why burnout has jumped an alarming 11 points in a single year

  4. Why nobody in tech would recommend their job to someone entering the industry today

  5. The #1 fear in tech right now (it’s not job loss to AI)

  6. Why managers are the single biggest lever for employee well-being

  7. Concrete advice for what employees and leaders can do right now


Brought to you by:

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Where to find Noam Segal:

• X: https://x.com/noamseg

• LinkedIn: https://www.linkedin.com/in/noamsegal

Referenced:

How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026

• How tech’s most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers

• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater

• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp

• NPS Is The Worst: https://www.npsistheworst.com

The Terminator: https://www.imdb.com/title/tt0088247

• Skynet: https://terminator.fandom.com/wiki/Skynet

• Inside Devin: The world’s first autonomous AI engineer that’s set to write 50% of its company’s code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu

• Devin: https://devin.ai

• An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union

• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5

• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble

• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste

• Building beautiful products with Stripe’s Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape

• Elon Musk: ‘Chances are we’re all living in a simulation’: https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation


Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.


My biggest takeaways from this conversation:

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7

How tech workers are feeling in 2026: a workforce splitting in two

Lenny's Newsletter · original → · 7/10 · AI: tech worker sentiment on AI adoption in 2026
👋 Hey there, I’m Lenny. Each week, I share deeply researched product, growth, and career advice. For more: Lenny’s Podcast | Lennybot | How I AI | Become an AI-Native Builder and other favorite…

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A year ago, we ran our first large-scale survey of how tech workers feel about their jobs and careers. We summed up what emerged in four words: burned out, but optimistic. Today we’re back with the results from our 2026 survey, and it’s a tale of two workforces.

One half feels amplified by AI—more capable, more confident, more excited than they’ve been in their entire career. The other half feels shaken by it—less sure of their value and whether there’s still a place for them. Which side of that line people fall on predicts how they feel about their career more than anything else, including their current role, seniority, company size, or any other measure we collected. The workforce is bifurcating into two realities.

But there’s more: Burnout overall jumped 11 points in a single year, and four in 10 respondents are worried about losing their job. Even those who feel optimistic about their own career may not recommend that friends follow their path. In the AI era, everyone agrees the ground is moving. No one is sure yet if it’s an earthquake or a launch.

We think these findings are important enough that we’re making this post free for everyone.

Let’s break it down.

Our biggest takeaways

  1. The workforce is splitting in two. Tech workers are either amplified by AI or shaken by it, and that divide shapes their feelings about work more than any title, tenure, or company.

  2. Burnout is surging, and optimism is fading. Significant burnout rose from 44.7% to 55.7% of respondents, while career optimism fell from 54.8% to 48.7%. Those who feel destabilized by AI are feeling the least optimistic and the most burned out. A worrisome trend.

  3. Tech workers wouldn’t recommend their own field. More than half (53%) would steer a newcomer away from a career in their role, even though they’re optimistic about their own future.

  4. Productivity is up, but quality is questionable. 82% say AI is making them measurably more productive, but many worry the gains are coming at the cost of the sharpness of the work and the worker.

  5. The underlying fear is of being overworked. Only 22% worry about “losing my job to AI.” Far more worry about being expected to do more for the same pay (51%), getting trapped in an unsustainable pace (46%), and the quality of their work going down (41%).

  6. Almost everyone is ambivalent. 77% of respondents picked at least one positive and one negative emotion about AI. The average person selected more than five emotions. The defining feeling about AI is ambivalence.

  7. Designers and researchers are the most worried. They report the most AI anxiety, the most fear of job loss, the worst-rated managers, and the lowest willingness to recommend their field. It’s a continuation of a trend we flagged last year.

  8. Founders are still the happiest people in tech, and small companies are still the best places to work. Both findings replicate from 2025, and both are statistically robust.

  9. Managers are still the biggest lever for happiness. Manager quality remains the strongest driver of burnout and one of the strongest drivers of everything else.

  10. The industry, in tech workers’ own words, is “chaotic.” Asked to describe the state of tech in a sentence, the most common theme by far was chaos, though the sentiment was split almost evenly between excitement and dread.

Takeaway 1: The workforce is splitting in two

To understand AI’s deeper impact on people, we asked an existential question: How has working with AI shifted how you see yourself as a professional? We gave respondents five options. Here’s how they responded:

  • “Amplified (I can do more, and better)”: 49.0%

  • “Redefined (My role is changing shape, but I don’t see that as clearly positive or negative)”: 27.4%

  • “Destabilized (I’m less sure where I stand or what’s really mine)”: 13.9%

  • “Diminished (I feel less essential or less valuable)”: 5.0%

  • “Unchanged”: 3.2%

When we lined up that question against the rest of the survey, the four identity groups differed dramatically:

As you go from “amplified” to “diminished,” optimism collapses, burnout climbs, layoff fear climbs, and willingness to recommend the field falls off. The people who feel amplified by AI are thriving. Those who feel diminished by it are in distress on every measure.

To make sure this wasn’t an artifact, we ran the numbers a few different ways:

  • In a regression pitting every variable against each other, AI-identity stance was the single strongest predictor of career optimism (standardized β = +0.39) and of whether someone would recommend their field (β = +0.60)—stronger than role, level, and company size combined.

  • As an effect size, the gap between the “amplified” and “diminished” groups on optimism is large (Cohen’s d ≈ 1.55). For context, the famously strong “founder effect” we’ll discuss later clocks in at d ≈ 0.56. The AI divide is roughly three times as large as that. It is, by a wide margin, the biggest effect in the dataset.

The question that best predicts how a tech worker feels about their work, in 2026, is no longer “What do you do?” or “Where do you work?” It’s “What has AI done to your sense of who you are?”

Meet the four tech workers of 2026

We did one more pass on this data: instead of using a single identity question, we clustered respondents based on the full pattern of emotions they reported about AI. Four types emerged, and you almost certainly recognize them.

The Energized (41%). The all-in adopters. They lead with “excited” (91%), “curious” (83%), and “hopeful” (59%). They’re the most optimistic group, the least burned out, and the only segment with a clearly positive read on their field. For them, AI truly seems like a superpower.

“Product has become fun again! You become an explorer, you play around . . . you spend long hours full of excitement. We’re in an amusement park.” —PM, Principal IC

The Conflicted (35%). The ambivalent center of gravity—and the largest group after the Amplified. Their signature emotions are “conflicted—holding positive and negative feelings at once” (68%)—and “curious” (64%), trailed closely by “overwhelmed” (56%) and “tired” (55%). They haven’t soured on AI; they’re just exhausted by the work of keeping up with it while holding two feelings at the same time.

“I’m simultaneously having the most fun I’ve had as a product builder and also feeling the most uncertainty I’ve felt. I’m confident I’ll be able to keep my skills sharp and adapt, but I’m not yet sure what it is that I’ll need to adapt into.” —PM, Senior IC

The Disoriented (12%). Defined almost entirely by one feeling: “disoriented—my role keeps shifting,” layered with “overwhelmed” (74%) and “tired” (73%). These are people watching their job change shape beneath them faster than they can find their footing again. They still think AI is somewhat useful. They’re not “refusers.” They’re just losing the thread of their role in the workplace.

“Things are so uncertain, we’re like farmers on the cusp of the industrial revolution. We know going into farming is the wisest long-term career choice, but we don’t see a clear path. This kind of uncertainty crowds out productivity.” —VP Product

The Resentful (12%). The burned-out and checked-out. Every one of them selected “resentful—I feel pressured to use AI,” and they cluster with “tired,” “conflicted,” and “overwhelmed.” They report the lowest optimism, the lowest willingness to recommend their field, and the lowest sense that AI is helping them at all. This is AI fatigue transformed into resistance.

“Tech overall kind of sucks right now. We used to adopt new technology because we were excited about the cool new things we could do. Now all we hear is ‘Use AI or you will lose your job’—and then people get fired anyway. I hate it.” —Director of Product

Takeaway 2: Burnout is surging, and optimism is fading

Significant burnout is now the majority experience for tech workers. 55.7% of working tech professionals report significant burnout—meaning they describe themselves as “moderately,” “very,” or “completely” burned out. Last year, that number was 44.7%. More than a quarter (26.2%) are now “very” or “completely” burned out.

Career optimism is dropping. Fewer than half (48.7%) of respondents are optimistic about the future of their career (down from 54.8% being optimistic last year). We’ve gone from “burned out but optimistic” in 2025 to “significantly burned out, and not that optimistic” a year later. We’re curious (and a little scared) about how this will look in a year.

That being said, job enjoyment is holding up: 42.6% enjoy their work “very much” or “extremely”; another 36.7% rate it “moderately”; and only about one in five (20.6%) enjoy it slightly or not at all.

Why the apparent contradiction? Enjoyment, burnout, and optimism are different constructs. Enjoyment is about the work itself, and people still like the work. Burnout is about pace, and people are increasingly worn out by how much they have to do. Optimism is about where things are heading. You can love your craft, be worn out by how much of it you’re doing, and feel doubt about the future all at once.

How worried are you about layoffs?

This year, we also added a question to the survey: How worried are you about being laid off in the next year?

41.2% are at least moderately worried, including 19.9% who are “very” or “extremely” worried. 28% aren’t worried at all. So roughly four in 10 tech workers are carrying real job-security anxiety into their week—a sizable undercurrent.

What makes layoff worry worth its own question is how tightly it’s bound to everything else. Of all the things we measured, layoff worry is the single strongest correlate of career pessimism (r = –0.47). Nothing else tracks negative outlook as closely. When people are scared of losing their jobs, their optimism goes first.

We’ll come back to who is most worried later. It’s not who you might guess.

Takeaway 3: Tech workers wouldn’t recommend their own field

This year, we asked an NPS-like question about people’s careers and roles: On a scale of 0 to 10, how likely are you to recommend a career in your role to a friend starting out today?

More than half of working tech professionals would actively steer a newcomer away from the path they chose. That translates to an average NPS score of –39. Moreover, a third of the people who call themselves optimistic still wouldn’t recommend their own field.

The cleanest way to say it: “The water’s fine; don’t come in.” People have largely made peace with their own trajectory. They’ve got the skills, the relationships, and the seniority to ride it out. But they’ve lost faith that the on-ramp still works for someone behind them.

“I’m lucky I’m later in my career . . . AI can augment what I’ve built. I think I won’t be in a position to hire and mentor new PMs, but I’ll be safe. Which feels really crappy to say.”

“I’m at the point where I can just retire and choose not to, so I’m not worried about my own career. But I’m worried about the younger generations.”

The recommendation score varies enormously by role, and the spread is its own story.

Founders would (just barely) still wave you in. Designers and researchers very much would not. And the score climbs steadily with seniority: senior and staff-level individual contributors are the least likely to recommend their field (both at NPS –49), while VPs (–23) and founders (–5) are the most. The further up you’ve climbed, the more the ladder still looks worth it; the people on the rungs below are the ones telling others not to start the climb.

Takeaway 4: Productivity is up, but quality is questionable

Given the rising burnout, the layoff anxiety, the doom in the discourse, you’d expect tech workers to be rather sour on AI. They’re not.

At the individual level, the AI numbers are among the most positive in the survey. 82% say AI is already making them at least moderately better at their job, and nearly half (49.4%) say “very much” or “extremely.” 60% feel confident or ahead of their peers in AI skills, compared with just 22.5% who feel anxious or behind.

But then we looked closer at what “better at my job” means. When we asked people to describe in their own words how AI had changed their work, “better” turned out to mean producing more and faster, but not higher quality. The productivity gains are coupled with deep unease about the costs of leveraging AI.

“I can do more, faster, but not better.”

“Amplified and destabilized at the same time. We just set a new denominator for the job. And it moves higher and higher every month.”

And the cost isn’t only in the quality of outputs. A striking number of people described their focus, their judgment, and their thinking as suffering:

“I’m amplified, but my brain is rotting, and my work feels worse.”

“I feel like I don’t think hard enough anymore—I just follow Claude. I don’t fully understand what I merge.”

“I miss feeling smart and having aha moments. I miss talking [to] and brainstorming [with] humans instead of machines.”

The productivity gains are real, but the quality of the work and the sharpness of the person producing it are taking a hit. The bar keeps rising to match what AI makes possible, and a growing share of people feel that neither the output nor their own mind is keeping up.

Takeaway 5: The underlying fear is of being overworked

Respondents’ number-one worry about AI’s impact on their career is the squeeze—AI raised the bar for output, and the reward was . . . more output expected, for the same paycheck.

They’re scared that the work will get harder, faster, and cheaper, and that they’ll be expected to keep smiling through it.

It feels like the dominant narrative about AI and work has been about replacement: the robots are coming for your job. Clearly, that’s not what tech workers are most afraid of. “Losing my job to AI” came in near the bottom of the list, at 22%.

Remember the “AI is replacing parts of my job” question? Half of the respondents say it’s happening to at least a moderate extent. You’d expect that feeling to drive layoff anxiety, but it doesn’t. The correlation between “AI is taking over parts of my job” and “I’m worried about being laid off” is essentially zero (r = +0.05).

What people are actually worried about is being asked to do more for the same pay, and watching the quality of their work slip.

It shows up vividly in the open-ended answers:

“More and more work is being handed off to me because I can use AI to get it done. But that makes it impossible to keep up with quality standards and not burn out.”

“AI helps with the toil, but then it’s also an enabler to do even more toil.”

“When we automate intellectual tasks, we’ll have to do high-value creative or strategic work only—doing that eight hours a day is not realistic. I used to take rest during repetitive tasks.”

This might sound like it contradicts the layoff worry from earlier. It doesn’t. People fear layoffs, but they mostly don’t blame AI for them. What they fear from AI is being buried in more work.

Add this all up, and you get a workforce that’s more productive than ever but quietly dreading what comes next. The speed AI unlocked got plowed straight back into expectations. Every gain becomes the new baseline, and the people expected to hit it are running out of room to breathe.

Takeaway 6: Almost everyone is ambivalent

If there’s one feeling that defines tech workers’ relationship with AI in 2026, it isn’t excitement, and it isn’t fear. It’s both, at the same time.

We asked people to check off every emotion that described how they feel about AI in their work. Here’s the full list, in order:

The two leaders are unambiguously positive (curious, excited). But the next cluster (if we ignore “conflicted”) is made up of people who are overwhelmed and tired. People are curious and overwhelmed. Excited and tired. Only 33% feel “hopeful,” even though 64% feel “excited.” Excitement about the present is running well ahead of hope about where this all goes.

Nikhyl Singhal named this phenomenon “smiling exhaustion.” The burnout of a few years ago was grim—all overhead and no agency. Today’s is different. People are shipping again, compensation has climbed, and many roles seem reborn. The catch is that there’s no off-switch: the tempo is brutal, and the rules rewrite themselves every month. It’s relentless, but it can also be exhilarating.

You see this in that 51% explicitly selected “holding positive and negative feelings at once.” But that undercounts the real ambivalence. When we looked at who picked at least one positive and at least one negative emotion, the number jumped to 77%. The average respondent selected five or more emotions (one person selected 13). It’s a workforce in which three out of four individuals are carrying a complex set of emotions about work.

Takeaway 7: Designers and researchers are the most worried

If AI is dividing the workforce, the obvious question is: along what lines? Who’s getting amplified, and who’s getting left behind?

The clearest pattern is by role: designers and researchers are at the epicenter of AI anxiety across the board, while founders and executives are feeling the best. We measured the share of each role that landed in negative identity or emotional buckets, and the spread is stark:

Among researchers, 51% are “anxious about my job security,” versus 15% of founders. Among designers, 63% feel “overwhelmed by the pace of change” and 61% feel “tired,” the highest of any role. Researchers are among the most likely to fear “losing my job to AI” (36%, just behind Data/Analytics at 38%), and designers are the most likely to feel the comp squeeze (61% selected “expected to do more for the same compensation”). Both report the lowest willingness to recommend their field of any role, and designers, as we’ll see, report the worst-rated managers in the survey.

Last year, designers and researchers showed the largest negative sentiment shift of any group. A year later, they’re the most negative on nearly every measure we have.

As a researcher, I’m acutely aware of the years of insecurities plaguing the research community. The biggest discussions for us have always been about getting a seat at the table and democratizing research across other functions. Many now feel the seat is being pulled from under us, and the work is being democratized, not to other roles but to AI.

By level, the most identity-destabilized group is early-career ICs (27%). (This is a wrinkle we’ll untangle in a moment, because those same early-career folks are, paradoxically, among the more optimistic.)

And the bigger the company, the more likely its people are to feel adrift in the AI transition: 23% feel destabilized at 10,000-plus-person companies, versus 15% at companies of 1 to 10.

AI is hardest on people in creative and research roles, on the most junior people, and on people working at the largest companies.

Takeaway 8: Founders are the happiest people in tech, and small companies are the best places to work

For all the AI upheaval, some of last year’s biggest findings came back almost unchanged, and their persistence through such a turbulent year makes them all the more convincing. Founders are still the happiest people in tech, and smaller companies are still better places to work than big ones. Before you read those as good news, it’s worth saying what “best” means here. The whole industry is sitting on a high baseline of burnout and a rather negative career view, and the winners of this section are the people who feel a little less of it.

Founders aren’t just the happiest people in tech—on most measures, they’re genuinely happy.

Founders and executives top nearly every measure in the survey: the highest optimism, the highest job enjoyment, the lowest burnout, the lowest layoff worry, and the most excitement about AI. That gap between founders and execs versus everyone else holds up statistically. On career optimism, it measures d ≈ 0.56, a medium-size effect and the second-largest in the entire dataset, behind only the AI divide.

As we wrote last year, the likeliest explanation is ownership: founders have the most control over their own destiny, and control turns out to be one of the best buffers against everything else. 71% are optimistic about their careers, they enjoy their work more than any other role, and they’re the least worried about layoffs of any group.

Ownership has limits, though. Nearly half of founders (47%) are still at least moderately burned out, with 18% very or completely burned out, even with the most control and the most upside of anyone in tech. And when we asked whether they’d recommend their path to a newcomer, founders landed at an NPS of –5. That is far healthier than the field’s –39, but it’s still bad. Even the happiest people in tech come out slightly net-negative on telling someone to follow their path.

One caveat: we only surveyed people who are founders today. The ones whose startups failed aren’t represented, and most startups don’t make it. Keep that in mind before you quit to go start something!

Smaller companies are still better places to work than big ones.

Company size predicts sentiment with almost eerie consistency. Walk from the smallest companies to the largest, and every measure of well-being gets steadily worse as the company grows:

People at small companies are more optimistic, less burned out, less worried about layoffs, and even feel AI is helping them more, likely because they have more freedom to actually use it. The “big-company blues” we described last year have settled in.

Look at the absolute numbers, though, not just the slope. Even at the smallest companies, 42% of people are at least moderately burned out, and the would-recommend score never climbs out of the red, sitting at –28 at 1-to-10-person shops. Small companies are winning a race to the least bad.

Two smaller echoes of 2025:

Where you physically work still hardly matters. There are barely any differences between how fully remote, hybrid, and in-office workers feel. Hybrid workers come out marginally the happiest (and in-office workers rate their managers the worst), but the gaps are small, just as we found last year. Employment type tells a familiar story with one twist. Founders and the self-employed are the happiest and least burned out, while contractors and freelancers are an interesting split—they are among the least burned out (less of the grind) but the most worried about layoffs (no job security).

One wrinkle you may have noticed: the largest companies (10,000+) tick up slightly in optimism and down in burnout compared with the 5,001–10,000 tier, breaking the smooth gradient. But neither difference is statistically significant (5,001–10,000 is our smallest sample), so the line flattens at the top rather than reversing. The one measure that does keep climbing to the very top is layoff worry. Workers at 10,000-plus-person companies are the most worried of anyone in the survey.

Takeaway 9: Managers are still the biggest lever for happiness

One more finding held firm from last year, and it may be the most actionable of all. Manager effectiveness remains the strongest driver of burnout in the entire dataset (it beats role, company size, and AI sentiment), and one of the strongest drivers of everything else. The gradient is dramatic:

Workers with an extremely effective manager report roughly 65% higher job enjoyment and dramatically lower burnout than those with an ineffective one. Yet only 25.5% of tech workers rate their manager as highly effective, while 36.5% rate theirs as ineffective, numbers that have barely budged since last year. The most powerful retention lever in tech is also the most neglected. (Notably, the worst-rated managers cluster in Data/Analytics and Design. The latter is a double blow, since designers are also among the most AI-anxious.)

Takeaway 10: The industry is “chaotic”

We asked, “In a sentence, how would you describe the state of the tech industry right now?” About 70% of respondents answered, and the single most common theme, by a wide margin, was chaos: roughly three in 10 explicitly used words like change, chaotic, uncertain, unstable, and in flux. Another one in six described an industry moving too fast to keep up with—treadmills, hamster wheels, hurricanes, “drinking from a firehose.” After that came AI hype and bubble talk (12%) and then, finally, a note of excitement and opportunity (11%).

A few responses capture the sentiment better than any percentage can:

“We’re in the 2nd inning of a massive shift, and no one knows how it will end, but all you can do is keep taking at-bats.”

“It feels like working on pure software is like picking up pennies in front of a steamroller.”

“The industry feels like it has lost its center of gravity—replacing curiosity about customers with an obsession over AI, automation, and efficiency.”

The chaos plus hype is well-described in this quote from a senior PM:

“Manic. Half are out of touch, clinging to the bandwagon, making the problem worse by pouring into the overhype. The other half are exhausted by the first half.” —Senior IC PM

When we ran sentiment analysis on the chaos-related quotes, the split was nearly even: 37% positive, 37% negative, and 26% neutral. The dominant theme is disorientation, but the emotional charge is truly bimodal. The same churn reads as thrilling to one person and terrifying to the next.

We confirmed this by splitting the responses by who wrote them. Career optimists and career pessimists describe the same industry in opposite terms. Optimists reach for “exciting,” “transforming,” “opportunity,” “fast-moving.” Pessimists reach for “chaos,” “layoffs,” “greed,” “dystopia.” Same disruption, opposite forecasts: half the room is anxiously bracing for AI’s impact; the other half is eagerly leaning into the AI era.

Where do we go from here?

The 2026 workforce is more burned out and less optimistic than a year ago, splitting along the fault line of AI into those who are thriving and those who are struggling, and a large, ambivalent middle caught between. Tech workers are mostly afraid of being squeezed by their jobs and increasing productivity expectations, privately convinced the field is no longer worth recommending to newcomers, while individually still finding real power and even joy in the tools. It’s a complicated moment. It’s also not a hopeless one.

So here’s what the data suggests you can actually do about it.

If you’re an employee:

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Items scoring 7/10 or above from 11 sources, scored by claude-haiku-4-5-20251001 on relevance to my interests. At most 3 per source.

Scoring categories & sources
  1. Local Wexford or South East Ireland news
  2. Irish or EU-wide affairs affecting citizens broadly: elections, new laws or policy being debated, cost of living, education — especially impacts on mid-life adults or teenagers. Never courts/crime stories.
  3. Irish news on a topic relevant to my interests
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  5. AI news including critical or anti-AI perspectives
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Sources: Breaking News Ireland, Wexford Local, Hacker News, r/gaming, r/pcgaming, r/antiAI, r/indiegaming, Lenny's Newsletter, One Useful Thing, Newcomer, Simon Willison