Navic pokud by byla dolozka k pracovne pravnimu vztahu (zamestnanec), nikoliv podle obchodniho zakona (dodavatel). Tak pokud nevymysleli nic extra, mimo toho co rika zakon, co by jim Moravec podepsal, melo by to byt platne pouze po dobu odstupneho… Daji se tam vymyslet ruzne klicky, ale dobry pravnik to rozcupuje.
Lidi často neví, s kým řešit svůj problém. Často ho neumí ani popsat. Chybí kontext, jasné zadání, někdy i představa, co vlastně chtějí.
Co je dobrý prompt pro AI? Přesně to co chybí..
@SemSuchar Jeho styl je nebezpecny, ale jak pro soupere tak pro nej samotneho. To stejne Jirkova hlava. + i - Treneri o tom vedi.
Jak ztrati plnej focus, svoji vzdalenost, prostor tak ma problem.
Blbe komentoval, ze vypustil fokus, kdyz sel dodelat zranene zvire. Podelal to jako Texeira..
Okay, fine :-/. Tasks, diffs, error checking, etc.—we’ve already implemented all of that on our platform.
Now, it's available on the main platform.
An impossible fight. Next up: agent orchestration—which could involve CPMA, the Viable System Model, …
Both Anthropic and OpenAI are gearing up to release updates to their desktop apps already next week.
Anthropic is finalizing its new Claude Code "epitaxy" experience with a power-user-friendly UI, Cowork-style layout, and the possibility to work on multiple repositories at once.
Users will be able to preview their code right in the app, along with sections to view a "Plan", "Tasks" executed by sub-agents, and "Diff".
But that's not all 👀
Are you using a special AI model? Does it influence you?
Time to check it out. New tools from Czech developers—such as Gemma4 — https://t.co/BtXorEwjjj
🚨 Today we're launching AI Scan!
AI learns from the entire internet. Then humans modify it in post training.
We measure how much that changes the answers. 🧵
Ano, je to tak, doslo k nepochopeni use-case. Omlouvam se, chytil jsem se spatne sekundarnich komentaru. Ano, v tom pripade to muze fungovat, asistentka je dobre uziti. Juniorni administrativa.
Tady si akorat hlidat riziko uniku dat, jak a k cemu to pripojuji, ale to stejne plati i pro zamestnance...
Pro vlastni uziti v cele rade use-case, kdyz si budes hlidat zakladni bezpecnost ok. Postavit nad tim firmu s “juniorem” a prodavat to klientum? 🤯🔫
Nevime co na tom delas - Report/research napr nemovitosti? Bozi.
MVP / Hrani si / experimenty? Ok. Neco interniho? Zalezi co. Production ready? Bez pudu sebezachovy..
Co je snadno nastavene zalohovani? Pravidelne ukladani zmen ai do git, presne popsane, kdy-kam se muze udelat revert? Jak mas osetrenou strukturu dat a samotna data v pripade revertu do daneho bodu. Mas nejake tools co klonuji data/db? Nebo to jsou jen MD soubory v adresari? Recovery delas ty nebo taky ta ai? …
Chapu tvoje nadseni, jen bych k tomu pristupoval s opatrnosti a respektem.
That won't work in the long term (in the age of ai). The problem is a bit more complicated. We spent some time on it. It’s not about review etc. This development approach has its limitations. People want to develop projects from scratch all the way up to large-scale projects without changing their approach or development method. It’s similar to micro, small, medium, and large companies—they’re all businesses, and there are many ways to manage them, but you can’t apply the same method to micro and small companies as you would to large ones…
It’s not about AI, but mindset and tools.
But GL & HF, I think you'll have plenty of clients before the market realizes that this approach to development has limits.
Some IT people are training to become carpenters.
I get it. But not me — I love wood, but I love engineering more.
Anthropic just released a study from millions of real AI conversations. Programmers top the list — AI already covers 75% of their tasks.
But that number only tells you one thing: where AI found early adoption. Not where it’ll hit hardest.
Writing code was always just part of engineering. A tool, not the goal.
Engineering is problem-solving through technology, math, and science. Decomposition. Systems thinking. Architecture.
Strong IT people won’t lose their jobs — they won’t allow it. It’s in their DNA. Different tool, same game.
The study says other industries are calm. Low exposure, no panic.
I’d be careful with that.
Manufacturing and automotive have been automating for decades. They know the drill. And now humanoid robots with AI are at the door — the same wave hitting the physical world that already hit the digital one.
AI learns where the data is and where users are willing. Right now, that’s IT. But precisely because IT adapts fast, the pressure will shift elsewhere. Better models. More training data. And industries outside the spotlight will find themselves unprepared — without the fast-adaptation culture IT has built.
The wave not reaching you yet doesn’t mean it won’t wash you out later.
IT people already know this. Others will figure it out — hopefully not the hard way.
Meanwhile, I’ll be designing systems and robots that solve problems digitally and physically.
#AI #engineering #FutureOfWork #automation
2025 vs 2026 from Chinese Spring Festival Gala shows speed of progress.
In 2025 they basically just stood.
In 2026, the robots transformed them into kung fu warriors, doing backflips, drunken fist, and nunchaku with seamless, pinpoint precision
@DavidGrudl Je vidět, že na pivu stále rád hodně povídáš 😂 (v dobrym, vždy fajn pokec).
Sumarizuji tvůj text: viděl si interstellar? Jak v tesseractu předával info dceři? Info v prostoru z ruzných pohledů, jen ještě trošku složitěji. Tak nějak +- tak .. 🤪😆
@VojtaRocek Klasika “2 % lidí myslí, 3 % lidí si myslí, že myslí, a 95 % lidí by raději zemřelo, než aby mysleli” … myslet bolí, raději to delegují na vybraného kolegu nebo šéfa co myslí za ně… => AI
Lidi bez znalostí, zkušeností, kontextu “halucinují” více než poslední LLM modely…
Stanford just made a $200,000 AI degree free.
No application.
No tuition.
No “elite access”.
Stanford released its actual AI/ML curriculum on YouTube.
Not a PR-friendly intro.
Not “AI for the public”.
This is the real thing.
The same lectures shaping people working on frontier models.
What just became public:
Deep Learning (CS230)
→ https://t.co/DUtL9MO6Y7
Transformers & LLMs (CME295)
→ https://t.co/gN57biwLsE
Language Models from Scratch (CS336)
→ https://t.co/GnH11pPBdW
ML from Human Feedback (CS329H)
→ https://t.co/X9nxEX6PNg
Computer Vision (CS231N)
→ https://t.co/oBxKKWZP22
LLM Evaluation & Scaling
→ https://t.co/1tDpw9ArTq
The uncomfortable truth:
The degree isn’t the scarce asset anymore.
Execution speed is.
Top schools know this.
That’s why they’re publishing the playbook.
👉 Bookmark this.
Comment the first lecture you’ll actually watch.