Sztuczna inteligencja w biznesie
Praktyczny przewodnik po narzędziach AI
ISBN: 9788328939868
Książka
Artur Strzelecki
Uniwersytet Ekonomiczny w Katowicach
1 Maja 50, 40-287
Katowice, Polska
Opis
Ta książka jest dla początkujących i średniozaawansowanych osób, które chcą realnie wykorzystać AI w firmie: właścicieli i menedżerów, product managerów, marketerów i e-commerce, specjalistów HR, konsultantów oraz analityków i osób odpowiedzialnych za wdrożenia (IT/operacje). Nie musisz programować -- nauczysz się skutecznego promptowania, pracy z asystentami i agentami oraz automatyzacji w narzędziach low-code/no-code, a także doboru modeli i architektury (chmura vs lokalnie, RAG) pod konkretne cele i ograniczenia. Zyskasz też praktyczne kryteria oceny kosztów, ryzyk, jakości, wymagań infrastrukturalnych i zgodności z regulacjami, żeby podejmować lepsze decyzje wdrożeniowe bez ulegania marketingowym rankingom.
Streszczenie:
Sztuczna inteligencja, odmieniana przez wszystkie przypadki i konteksty, stała się w ostatnich latach jednym z najmocniej dyskutowanych tematów. Powstają liczne artykuły poświęcone fenomenowi AI, opasłe tomiszcza dotyczące jej rozwoju i publikacje odnoszące się do przyszłości, która nas w związku z tym czeka.
Jednak ten poradnik, choć nie unika niezbędnej teorii, jest zupełnie inny.
Stawiam w nim na praktykę, łączę podstawy technologiczne z kwestiami biznesowymi, pokazuję zarówno mechanizmy działania współczesnych systemów AI, jak i sposoby ich zastosowania w firmach i organizacjach. Za sprawą tej książki poznasz nie tylko możliwości poszczególnych narzędzi, lecz także związane z nimi ograniczenia, koszty, ryzyka, wymagania infrastrukturalne, konsekwencje organizacyjne i regulacyjne.
Krótka notka o autorze:
Artur Strzelecki jest profesorem Uniwersytetu Ekonomicznego w Katowicach i pracownikiem Katedry Informatyki, specjalizującym się w marketingu internetowym, analityce internetowej, wyszukiwarkach internetowych oraz zastosowaniach sztucznej inteligencji w biznesie. Czterokrotnie znalazł się w światowym rankingu TOP 2% naukowców według Stanford University. Jest autorem licznych publikacji naukowych w renomowanych czasopismach międzynarodowych oraz uczestnikiem projektów badawczych i konferencji na kilku kontynentach. W swojej działalności badawczej i dydaktycznej koncentruje się na wpływie AI na edukację, organizacje i biznes, prowadząc m.in. kurs „Narzędzia AI w biznesie". Od ponad dekady współtworzy Festiwal SEO – największą w Polsce konferencję poświęconą optymalizacji stron internetowych – oraz współpracuje z przedsiębiorstwami w zakresie strategii widoczności online, analityki internetowej i marketingu cyfrowego.
Spis treści:
PDF
Kup książkę online
Link do księgarni
Dane bibliograficzne:
Wydanie drukowane: ISBN 978-83-289-3986-8, premiera 17 listopada 2026, format 165 × 235 mm. E-book: ISBN 978-83-289-4450-3, data wydania: 7 października 2026.
Strzelecki, A.(2026). Sztuczna inteligencja w biznesie. Praktyczny przewodnik po narzędziach AI. Wydawnictwo Naukowe Helion.
Bibliografia:
- Abdul Razak, S. F., Phey Ernn, Y., Yussoff, F. I., Ali Bukar, U., i Yogarayan, S. (2024). Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model. Journal of Human, Earth, and Future, 5(1), 85–99. https://doi.org/10.28991/HEF-2024-05-01-07
- Acharya, D. B., Kuppan, K., i Divya, B. (2025). Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey. IEEE Access, 13, 18912–18936. https://doi.org/10.1109/ACCESS.2025.3532853
- Almada, M., i Radu, A. (2024). The Brussels Side-Effect: How the AI Act Can Reduce the Global Reach of EU Policy. German Law Journal, 25(4), 646–663. https://doi.org/10.1017/glj.2023.108
- Alsaadi, H. A., Radain, D. T., Alzahrani, M. M., Alshammari, W. F., Alahmadi, D., i Fakieh, B. (2021). Factors that affect the utilization of low-code development platforms: survey study. Romanian Journal of Information Technology and Automatic Control, 31(3), 123–140. https://doi.org/10.33436/v31i3y202110
- Amir, A. R., i Atif, S. M. (2026). Evaluating Workflow Automation Efficiency Using n8n: A Small-Scale Business Case Study. ArXiv. http://arxiv.org/abs/2602.01311
- Anam, R. K. (2025). Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity. ArXiv. http://arxiv.org/abs/2507.18638
- Anthropic. (2026). Introducing Claude Design by Anthropic Labs. https://www.anthropic.com/news/claude-design-anthropic-labs
- Antrophic. (2024). Introducing the Model Context Protocol. https://www.anthropic.com/news/model-context-protocol
- Anything. (2026). Anything. https://www.createanything.com/
- ARC Prize Foundation. (2026). ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence. ArXiv, 1–21. http://arxiv.org/abs/2603.24621
- Arkhipkin, V., Filatov, A., Vasilev, V., Maltseva, A., Azizov, S., Pavlov, I., Agafonova, J., Kuznetsov, A., i Dimitrov, D. (2023). Kandinsky 3.0 Technical Report. ArXiv.
- Arora, S., i Hastings, J. (2025). Securing Agentic AI Systems — A Multilayer Security Framework. 2025 5th International Conference on Robotics, Automation, and Artificial Intelligence, 509–514. https://doi.org/10.1109/RAAI67517.2025.11423374
- Artificial Analysis. (2026). Artificial Analysis. https://artificialanalysis.ai/
- Arulraj, A., Chandra, A., Chen, W., Fan, R., Guo, S., He, X., Jiang, Z., Ku, M., Li, T., Ma, X., Ni, Y., Ren, W., Wang, K., Wang, Y., Yue, X., Zhang, G., i Zhuang, A. (2024). MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark. Advances in Neural Information Processing Systems, 95266–95290. https://doi.org/10.52202/079017-3018
- Aura. (2026). Aura. https://www.aura.build/
- Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., Hui, B., Ji, L., Li, M., Lin, J., Lin, R., Liu, D., Liu, G., Lu, C., Lu, K., … Zhu, T. (2023). Qwen Technical Report. ArXiv. http://arxiv.org/abs/2309.16609
- Bai, Y., Tu, S., Zhang, J., Peng, H., Wang, X., Lv, X., Cao, S., Xu, J., Hou, L., Dong, Y., Tang, J., i Li, J. (2025). LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, 3639–3664. https://doi.org/10.18653/v1/2025.acl-long.183
- Bandara, E., Gore, R., Shetty, S., Rajapakse, S., Kularathna, I., Karunarathna, P., Mukkamala, R., Foytik, P., Bouk, S. H., Rahman, A., Liang, X., Hass, A., Hewa, T., Keong, N. W., De Zoysa, K., Withanage, A., i Loganathan, N. (2026). A Practical Guide to Agentic AI Transition in Organizations. ArXiv. http://arxiv.org/abs/2602.10122
- Bandi, C., Hertzberg, B., Boo, G., Polakam, T., Da, J., Hassaan, S., Sharma, M., Park, A., Hernandez, E., Rambado, D., Salazar, I., Cruz, R., Rane, C., Levin, B., Kenstler, B., i Liu, B. (2026). MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers. ArXiv. http://arxiv.org/abs/2602.00933
- Bansod, P. B. (2025). Distinguishing Autonomous AI Agents from Collaborative Agentic Systems: A Comprehensive Framework for Understanding Modern Intelligent Architectures. ArXiv. http://arxiv.org/abs/2506.01438
- Bar-Tal, O., Yariv, L., Lipman, Y., i Dekel, T. (2023). MultiDiffusion: fusing diffusion paths for controlled image generation. Proceedings of the 40th International Conference on Machine Learning, ICML’23.
- Barova, M. (2026). Best AI Video Generators in 2026. https://higgsfield.ai/blog/best-ai-video-generators-2026
- Base44. (2026). Base44. https://base44.com/
- Baughan, A., Wang, X., Liu, A., Mercurio, A., Chen, J., i Ma, X. (2023). A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3544548.3581152
- Bielik.AI. (2025). Jak prosto uruchomić Bielika na swoim komputerze? https://bielik.ai/bielik-lokalnie/
- Black Forest Labs, Batifol, S., Blattmann, A., Boesel, F., Consul, S., Diagne, C., Dockhorn, T., English, J., English, Z., Esser, P., Kulal, S., Lacey, K., Levi, Y., Li, C., Lorenz, D., Müller, J., Podell, D., Rombach, R., Saini, H., … Smith, L. (2025). FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space. ArXiv. http://arxiv.org/abs/2506.15742
- Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., i Kreis, K. (2023). Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 22563–22575. https://doi.org/10.1109/CVPR52729.2023.02161
- Bolt. (2026). Bolt. https://bolt.new/
- Brohs, D., Łubińska, A., Kania, I., Tokarska, K., i Biernatowska, A. (2026). Sztuczna inteligencja w firmach: gotowość do adopcji, kompetencje i potrzeby.
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
- Bsharat, S. M., Myrzakhan, A., i Shen, Z. (2024). Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4. ArXiv. http://arxiv.org/abs/2312.16171
- ByteDance. (2025). Seed-OSS Open-Source Models Release. https://seed.bytedance.com/en/blog/seed-oss-open-source-models-release
- Cai, H., Cao, S., Du, R., Gao, P., Hoi, S., Hou, Z., Huang, S., Jiang, D., Jin, X., Li, L., Li, Z., Li, Z.-Y., Liu, D., Liu, D., Shi, J., Wu, Q., Yu, F., Zhang, C., Zhang, S., i Zhou, S. (2025). Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer. ArXiv. http://arxiv.org/abs/2511.22699
- Canva. (2026). Canva. https://www.canva.com/
- Cao, R., Chen, J., Cheng, Z., Hua, T., Lei, F., Li, X., Liu, Y., Savarese, S., Shin, D., Xie, T., Xiong, C., Xu, Y., Yu, T., Zhang, D., Zhao, S., Zhong, V., i Zhou, S. (2024). OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments. Advances in Neural Information Processing Systems, 37, 52040–52094. https://doi.org/10.52202/079017-1650
- Cao, S., Chen, H., Chen, P., Cheng, Y., Cui, Y., Deng, X., Dong, Y., Gong, K., Gu, T., Gu, X., Hang, T., Huang, D., Jiang, J., Jiang, Z., Kong, W., Li, C., Li, D., Li, J., Li, X., … Zhong, Z. (2026). HunyuanImage 3.0 Technical Report. ArXiv. http://arxiv.org/abs/2509.23951
- Catanzaro, B. (2025). Open Secret: How NVIDIA Nemotron Models, Datasets and Techniques Fuel AI Development. https://blogs.nvidia.com/blog/nemotron-open-source-ai/
- Chalmers, D., Hunt, R. ‘Rick,’ Pachidi, S., Potočnik, K., i Townsend, D. (2026). The Acceleration of Artificial Intelligence: Rethinking Organization and Work in an Era of Rapid Technological Change. Journal of Management Studies, 63(2), 285–314. https://doi.org/10.1111/joms.70063
- Chang, T. A., Arnett, C., Eldesokey, A., Sadallah, A., Kashar, A., Daud, A., Olanihun, A. G., Mohammed, A. L., Praise, A., Sharma, A. M., Gupta, A., Iyigun, A., Simplício, A., Essouaied, A., Chorana, A., Eppa, A., Oladipo, A., Ramesh, A., Dorkin, A., … Adelani, D. I. (2025). Global PIQA: Evaluating Physical Commonsense Reasoning Across 100+ Languages and Cultures. ArXiv. http://arxiv.org/abs/2510.24081
- Chen, M., Dai, C., Dong, X., Feng, C., Fu, K., Li, J., Peng, Z., Tong, Y., Zhang, J., i Zhu, H. (2025). Dingtalk DeepResearch: A Unified Multi Agent Framework for Adaptive Intelligence in Enterprise Environments. ArXiv. http://arxiv.org/abs/2510.24760
- Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. de O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., … Zaremba, W. (2021). Evaluating Large Language Models Trained on Code. ArXiv. http://arxiv.org/abs/2107.03374
- Chen, V., Talwalkar, A., Brennan, R., i Neubig, G. (2026). Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1–19. https://doi.org/10.1145/3772318.3790850
- Cheng, Z., Wohnig, S., Gupta, R., Alam, S., Abdullahi, T., Ribeiro, J. A., Nielsen-Garcia, C., Mir, S., Li, S., Orender, J., Bahrainian, S. A., Kirste, D., Gokaslan, A., Glinka, M., Eickhoff, C., i Wolff, R. (2025). Benchmarking is Broken — Don’t Let AI be its Own Judge. ArXiv. http://arxiv.org/abs/2510.07575
- Chia, O., i Calder, E. (2026). OpenAI closes Sora video-making app and cancels $1bn Disney deal. https://www.bbc.com/news/articles/c3w3e467ewqo
- Cho, A., Kim, G. C., Karpekov, A., Helbling, A., Wang, Z. J., Lee, S., Hoover, B., i Chau, D. H. (Polo). (2025). TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models. Proceedings of the AAAI Conference on Artificial Intelligence, 39(28), 29625–29627. https://doi.org/10.1609/aaai.v39i28.35347
- Chollet, F. (2019). On the Measure of Intelligence. ArXiv. http://arxiv.org/abs/1911.01547
- Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., i Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. W: Medical Image Computing and Computer-Assisted Intervention (s. 424–432). https://doi.org/10.1007/978-3-319-46723-8_49
- Corporate Europe Observatory. (2025). Bias baked in: How Big Tech sets its own AI standards.
- Cosmic. (2026). Cosmic. https://www.cosmic.new/
- Cottier, B., Rahman, R., Fattorini, L., Maslej, N., Besiroglu, T., i Owen, D. (2025). The rising costs of training frontier AI models. ArXiv. https://doi.org/10.48550/arXiv.2405.21015
- Crane, J. (2026). An AI agent just destroyed our production data. It confessed in writing. X. https://x.com/lifeof_jer/article/2048103471019434248
- Crevier, D. (1993). AI: the tumultuous history of the search for artificial intelligence. Basic Books, Inc.
- Cui, H., i Yasseri, T. (2024). AI-enhanced collective intelligence. Patterns, 5(11), 101074. https://doi.org/10.1016/j.patter.2024.101074
- Curty, S., Härer, F., i Fill, H.-G. (2023). Design of blockchain-based applications using model-driven engineering and low-code/no-code platforms: a structured literature review. Software and Systems Modeling, 22(6), 1857–1895. https://doi.org/10.1007/s10270-023-01109-1
- da Cruz, M. A. A., de Paula, H. T. L., Caputo, B. P. G., Mafra, S. B., Lorenz, P., i Rodrigues, J. J. P. C. (2021). OLP—A RESTful Open Low-Code Platform. Future Internet, 13(10), 249. https://doi.org/10.3390/fi13100249
- Daniel, G., Cabot, J., Deruelle, L., i Derras, M. (2020). Xatkit: A Multimodal Low-Code Chatbot Development Framework. IEEE Access, 8, 15332–15346. https://doi.org/10.1109/ACCESS.2020.2966919
- De Peuter, S., i Kaski, S. (2023). Zero-Shot Assistance in Sequential Decision Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 11551–11559. https://doi.org/10.1609/aaai.v37i10.26365
- Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H., Shu, T., Song, M., Xing, E. P., i Hu, Z. (2022). RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning. ArXiv. http://arxiv.org/abs/2205.12548
- Deng, X., Da, J., Pan, E., He, Y. Y., Ide, C., Garg, K., Lauffer, N., Park, A., Pasari, N., Rane, C., Sampath, K., Krishnan, M., Kundurthy, S., Hendryx, S., Wang, Z., Bharadwaj, V., Holm, J., Aluri, R., Zhang, C. B. C., … Kenstler, B. (2025). SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? ArXiv. http://arxiv.org/abs/2509.16941
- Deng, Z., Guo, Y., Han, C., Ma, W., Xiong, J., Wen, S., i Xiang, Y. (2025). AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways. ACM Computing Surveys, 57(7), 1–36. https://doi.org/10.1145/3716628
- Dhariwal, P., i Nichol, A. (2021). Diffusion Models Beat GANs on Image Synthesis. ArXiv. http://arxiv.org/abs/2105.05233
- Ding, J., Long, S., Pu, C., Zhou, H., Gao, H., Gao, X., He, C., Hou, Y., Hu, F., Li, Z., Shi, W., Wang, Z., Zan, D., Zhang, C., Zhang, X., Chen, Q., Cheng, X., Deng, B., Gu, Q., … Zhang, G. (2026). NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents. ArXiv. http://arxiv.org/abs/2512.12730
- Du, X., Yao, Y., Ma, K., Wang, B., Zheng, T., Zhu, K., Liu, M., Liang, Y., Jin, X., Wei, Z., Zheng, C., Deng, K., Gavin, S., Jia, S., Jiang, S., Liao, Y., Li, R., Li, Q., Li, S., … Zhang, G. (2025). SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines. ArXiv. http://arxiv.org/abs/2502.14739
- Elshan, E., Siemon, D., Bruhin, O., Kedziora, D., i Schmidt, N. (2024). Unveiling Challenges and Opportunities in Low Code Development Platforms: A StackOverflow Analysis. Proceedings of the 57th Hawaii International Conference on System Sciences, 7269–7279. https://doi.org/10.24251/HICSS.2024.872
- Emergent. (2026). Emergent. https://app.emergent.sh/landing/
- Eriksson, M., Purificato, E., Noroozian, A., Vinagre, J., Chaslot, G., Gomez, E., i Fernandez-Llorca, D. (2025). Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation. ArXiv. http://arxiv.org/abs/2502.06559
- Evans, Z., Parker, J. D., Carr, C., Zukowski, Z., Taylor, J., i Pons, J. (2025). Stable Audio Open. 2025 IEEE International Conference on Acoustics, Speech and Signal Processing, 1–5. https://doi.org/10.1109/ICASSP49660.2025.10888461
- Farabet, C., i Lacombe, O. (2026). Gemma 4: Byte for byte, the most capable open models. https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/
- Ferrucci, D., Brown, E., Chu‐Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J., Schlaefer, N., i Welty, C. (2010). Building Watson: An Overview of the DeepQA Project. AI Magazine, 31(3), 59–79. https://doi.org/10.1609/aimag.v31i3.2303
- Figma. (2026). Figma. https://www.figma.com/
- Floot. (2026). Floot. https://floot.com/en
- Gao, T., Fisch, A., i Chen, D. (2021). Making Pre-trained Language Models Better Few-shot Learners. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, 3816–3830. https://doi.org/10.18653/v1/2021.acl-long.295
- Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., Silver, D., Johnson, M., Antonoglou, I., Schrittwieser, J., Glaese, A., Chen, J., Pitler, E., Lillicrap, T., Lazaridou, A., … Vinyals, O. (2025). Gemini: A Family of Highly Capable Multimodal Models. ArXiv. http://arxiv.org/abs/2312.11805
- Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., Mariooryad, S., Ding, Y., Geng, X., Alcober, F., Frostig, R., Omernick, M., Walker, L., Paduraru, C., Sorokin, C., … Vinyals, O. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. ArXiv. http://arxiv.org/abs/2403.05530
- Ghalebikesabi, S., Bagdasaryan, E., Yi, R., Yona, I., Shumailov, I., Pappu, A., Shi, C., Weidinger, L., Stanforth, R., Berrada, L., Kohli, P., Huang, P.-S., i Balle, B. (2024). Operationalizing Contextual Integrity in Privacy-Conscious Assistants. ArXiv. http://arxiv.org/abs/2408.02373
- Gobira, M., Nakayama, L. F., Regatieri, C. V. S., i Belfort, R. (2025). Comparing No-Code Platforms and Deep Learning Models for Glaucoma Detection From Fundus Images. Cureus, 17(3), e81064. https://doi.org/10.7759/cureus.81064
- Goknil, A., Gelderblom, F. B., Tverdal, S., Tokas, S., i Song, H. (2024). Privacy Policy Analysis through Prompt Engineering for LLMs. ArXiv. http://arxiv.org/abs/2409.14879
- Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., i Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27.
- Google. (2026). NotebookLM. Google Blog. https://blog.google/innovation-and-ai/products/notebooklm/
- Google AI Studio. (2026). Google AI Studio. https://aistudio.google.com/prompts/new_chat
- GovTech Singapore. (2023). Prompt Engineering Playbook (Beta v3). https://www.developer.tech.gov.sg/products/collections/data-science-and-artificial-intelligence/playbooks/prompt-engineering-playbook-beta-v3.pdf
- Guo, Z., Leng, Y., Wu, Y., Zhao, S., i Tan, X. (2023). Prompttts: Controllable Text-To-Speech With Text Descriptions. 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, 1–5. https://doi.org/10.1109/ICASSP49357.2023.10096285
- Gupta, N., Chatterjee, R., Haas, L., Tao, C., Wang, A., Liu, C., Oiwa, H., Gribovskaya, E., Ackermann, J., Blitzer, J., Goldshtein, S., i Das, D. (2026). DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents. ArXiv. http://arxiv.org/abs/2601.20975
- He, W., Sun, Y., Hao, H., Hao, X., Xia, Z., Gu, Q., Han, C., Zhao, D., Su, H., Zhang, K., Gao, M., Su, X., Cai, X., Cai, X., Yang, Y., i Zhao, Y. (2025). VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications. ArXiv. http://arxiv.org/abs/2509.26490
- Hern, A. (2024). TechScape: How cheap, outsourced labour in Africa is shaping AI English. The Guardian. https://www.theguardian.com/technology/2024/apr/16/techscape-ai-gadgest-humane-ai-pin-chatgpt
- Heuschkel, S. (2023). The impact of no-code on digital product development. ArXiv. http://arxiv.org/abs/2307.16717
- Ho, A., Denain, J.-S., Atanasov, D., Albanie, S., i Shah, R. (2025). A Rosetta Stone for AI Benchmarks. ArXiv. http://arxiv.org/abs/2512.00193
- Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., i Salimans, T. (2022). Imagen Video: High Definition Video Generation with Diffusion Models. ArXiv. http://arxiv.org/abs/2210.02303
- Ho, J., Jain, A., i Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. ArXiv. http://arxiv.org/abs/2006.11239
- Hong, H., Xiao, C., Wang, Y., Liu, Y., Rong, W., i Lin, C. (2025). Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts. ArXiv. http://arxiv.org/abs/2504.21117
- Hsieh, C.-P., Sun, S., Kriman, S., Acharya, S., Rekesh, D., Jia, F., Zhang, Y., i Ginsburg, B. (2024). RULER: What’s the Real Context Size of Your Long-Context Language Models? ArXiv. http://arxiv.org/abs/2404.06654
- Hu, K. (2023). ChatGPT sets record for fastest-growing user base – analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
- Hu, Q., Xiao, Q., Cao, H., i Shen, H. (2026). When Your Boss Is an AI Bot: Exploring Opportunities and Risks of Manager Clone Agents in the Future Workplace. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems, 1–21. https://doi.org/10.1145/3772318.3790987
- Huang, Y., Bai, Y., Zhu, Z., Zhang, J., Zhang, J., Su, T., Liu, J., Lv, C., Zhang, Y., Lei, J., Fu, Y., Sun, M., i He, J. (2023). C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models. Advances in Neural Information Processing Systems, 36, 62991–63010.
- Hugging Face. (2026). Security incident disclosure — July 2026. https://huggingface.co/blog/security-incident-july-2026
- Ikenoue, Y., Tashiro, H., i Kuroyanagi, S. (2025). Automatic Prompt Generation via Adaptive Selection of Prompting Techniques. ArXiv. http://arxiv.org/abs/2510.18162
- Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. de las, Hanna, E. B., Bressand, F., Lengyel, G., Bour, G., Lample, G., Lavaud, L. R., Saulnier, L., Lachaux, M.-A., Stock, P., Subramanian, S., Yang, S., … Sayed, W. El. (2024). Mixtral of Experts. ArXiv. http://arxiv.org/abs/2401.04088
- Jiang, E., Olson, K., Toh, E., Molina, A., Donsbach, A., Terry, M., i Cai, C. J. (2022). PromptMaker: Prompt-based Prototyping with Large Language Models. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–8. https://doi.org/10.1145/3491101.3503564
- Jiang, Z., Gao, S., Tang, Q., Wang, Z., Liu, Y., i Huang, H.-Z. (2026). Generative Reliability-Based Design Optimization Using In-Context Learning Capabilities of Large Language Models. Journal of Mechanical Design, 148(2), 021713. https://doi.org/10.1115/1.4069598
- Jiang, Z., Xu, F. F., Araki, J., i Neubig, G. (2020). How Can We Know What Language Models Know? Transactions of the Association for Computational Linguistics, 8, 423–438. https://doi.org/10.1162/tacl_a_00324
- Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., i Narasimhan, K. (2024). SWE-bench: Can Language Models Resolve Real-World GitHub Issues? ArXiv. http://arxiv.org/abs/2310.06770
- Jones, C. R., i Bergen, B. K. (2025). Large Language Models Pass the Turing Test. ArXiv. http://arxiv.org/abs/2503.23674
- Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., … Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
- Kalai, A. T., Nachum, O., Vempala, S. S., i Zhang, E. (2025). Why Language Models Hallucinate. ArXiv. http://arxiv.org/abs/2509.04664
- Karcz, Z. (2026). Samoregulacja czy przymus? Ewolucja polityk sztucznej inteligencji w kontekście unijnego zarządzania ryzykiem. W: T. Doligalski i D. Kaszyński (red.), Sztuczna inteligencja w naukach ekonomicznych. Difin.
- Karpathy, A. (2025). There’s a new kind of coding I call “vibe coding.” X. https://x.com/karpathy/status/1886192184808149383
- Kasparow, G. (2021). Ostatni bastion umysłu: Szachy, sztuczna inteligencja i kreatywność człowieka. Świat Książki.
- Käss, S., Strahringer, S., i Westner, M. (2023). Practitioners’ Perceptions on the Adoption of Low Code Development Platforms. IEEE Access, 11, 29009–29034. https://doi.org/10.1109/ACCESS.2023.3258539
- Kepel, D., i Valogianni, K. (2024). Autonomous Prompt Engineering in Large Language Models. ArXiv. http://arxiv.org/abs/2407.11000
- Kijko, P. (2025). Edytory AI. W: SEO w praktyce (s. 558–579).
- Komisja Europejska. (2025). AI Continent Action Plan COM(2025)165. https://ec.europa.eu/newsroom/dae/redirection/document/114523
- Kreuk, F., Synnaeve, G., Polyak, A., Singer, U., Défossez, A., Copet, J., Parikh, D., Taigman, Y., i Adi, Y. (2023). AudioGen: Textually Guided Audio Generation. ArXiv. http://arxiv.org/abs/2209.15352
- Krizhevsky, A., Sutskever, I., i Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
- Ładna, A., Rosłaniec, K., Marcisz, J., Błażej, M., Kamiński, K., Konopczyński, F., Troszyński, M., Bieliński, J., i Jankiewicz, A. (2026). AI w e-administracji publicznej – perspektywa urzędników i instytucji.
- Laskar, M. T. R., Alqahtani, S., Bari, M. S., Rahman, M., Khan, M. A. M., Khan, H., Jahan, I., Bhuiyan, A., Tan, C. W., Parvez, M. R., Hoque, E., Joty, S., i Huang, J. (2024). A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 13785–13816. https://doi.org/10.18653/v1/2024.emnlp-main.764
- LeCun, Y., Bengio, Y., i Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Lemkin, J. (2025). Vibe Coding Day 8. X. https://x.com/jasonlk/status/1945840482019623082
- Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., i Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
- Li, D., Kamko, A., Akhgari, E., Sabet, A., Xu, L., i Doshi, S. (2024). Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation. ArXiv. http://arxiv.org/abs/2402.17245
- Li, H., Qu, L., Hu, J., i Li, T. (2025). EME-TTS: Unlocking the Emphasis and Emotion Link in Speech Synthesis. ArXiv. http://arxiv.org/abs/2507.12015
- Li, J., Zhao, W., Zhao, J., Zeng, W., Wu, H., Wang, X., Ge, R., Cao, Y., Huang, Y., Liu, W., Liu, J., Su, Z., Guo, Y., Zhou, F., Zhang, L., Michelini, J., Wang, X., Yue, X., Zhou, S., … He, J. (2026). The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution. ArXiv. http://arxiv.org/abs/2510.25726
- Li, R. (2026). Realistic AI Video Prompting in 2026: 10 Prompt Pillars + 10 Examples. https://magichour.ai/blog/realistic-ai-video-prompting
- Li, X., Chen, W., Liu, Y., Zheng, S., Chen, X., He, Y., Li, Y., You, B., Shen, H., Sun, J., Wang, S., Li, B., Zeng, Q., Wang, D., Zhao, X., Wang, Y., Chaim, R. Ben, Di, Z., Gao, Y., … Lee, H. (2026). SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks. ArXiv. http://arxiv.org/abs/2602.12670
- Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., Dai, D., Guo, D., Yang, D., Chen, D., Ji, D., Li, E., Lin, F., Dai, F., Luo, F., … Pan, Z. (2025). DeepSeek-V3 Technical Report. ArXiv. http://arxiv.org/abs/2412.19437
- Liu, H., Chen, Z., Yuan, Y., Mei, X., Liu, X., Mandic, D., Wang, W., i Plumbley, M. D. (2023). AudioLDM: text-to-audio generation with latent diffusion models. Proceedings of the 40th International Conference on Machine Learning.
- Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., i Neubig, G. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9), 1–35. https://doi.org/10.1145/3560815
- Liu, V., i Chilton, L. B. (2022). Design Guidelines for Prompt Engineering Text-to-Image Generative Models. CHI Conference on Human Factors in Computing Systems, 1–23. https://doi.org/10.1145/3491102.3501825
- Liu, Y., Zhang, K., Li, Y., Yan, Z., Gao, C., Chen, R., Yuan, Z., Huang, Y., Sun, H., Gao, J., He, L., i Sun, L. (2024). Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models. ArXiv. http://arxiv.org/abs/2402.17177
- Llama. (2023). Industry Leading, Open-Source AI. https://www.llama.com/
- Loveable. (2026). Loveable. https://lovable.dev/
- Macaly. (2026). Macaly. https://www.macaly.com/
- Magic Patterns. (2026). Magic Patterns. https://www.magicpatterns.com/
- Masad, A. (2025). We saw Jason’s post. @Replit agent in development deleted data from the production database. X. https://x.com/amasad/status/1946986468586721478
- Matook, S., Maggie Wang, Y., Koeppel, N., i Guerin, S. (2024). Metacognitive skills in low-code app development: Work-integrated learning in information systems development. Journal of Information Technology, 39(1), 41–70. https://doi.org/10.1177/02683962231170238
- McCarthy, J., Minsky, M. L., Rochester, N., i Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27(4), 12–14. https://doi.org/10.1609/aimag.v27i4.1904
- McCulloch, W.S., i Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
- Midjourney. (2026a). Character Reference and Style Reference Parameters. https://docs.midjourney.com/hc/en-us/articles/32162917505293-Character-Reference
- Midjourney. (2026b). Parameter List. https://docs.midjourney.com/hc/en-us/articles/32859204029709-Parameter-List
- Misra, A., Wang, J., McCullers, S., White, K., i Ferres, J. L. (2025). Measuring AI Diffusion: A Population-Normalized Metric for Tracking Global AI Usage. ArXiv. http://arxiv.org/abs/2511.02781
- Mistral AI. (2025). Introducing Mistral 3. https://mistral.ai/news/mistral-3
- Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., i Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596
- Mocha. (2026). Mocha. https://getmocha.com/
- Model Context Protocol. (2026). What is the Model Context Protocol (MCP)? https://modelcontextprotocol.io/docs/getting-started/intro
- Morales, S., Clarisó, R., i Cabot, J. (2025). Impromptu: a framework for model-driven prompt engineering. Software and Systems Modeling, 24(6), 1627–1645. https://doi.org/10.1007/s10270-024-01235-4
- Moskal, M. (2021). No-code application development on the example of Logotec App Studio platform. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 11(1), 54–57. https://doi.org/10.35784/iapgos.2429
- Nguyen, W., Luong, V., i Nguyen, C. (2025). ProSEA: Problem Solving via Exploration Agents. ArXiv. http://arxiv.org/abs/2510.07423
- Nussbaum, Z., Morris, J. X., Duderstadt, B., i Mulyar, A. (2025). Nomic Embed: Training a Reproducible Long Context Text Embedder. ArXiv. http://arxiv.org/abs/2402.01613
- OpenAI. (2025a). GPT Images: Prompting Guide. https://developers.openai.com/cookbook/examples/multimodal/image-gen-1.5-prompting_guide
- OpenAI. (2025b). Przedstawiamy modele gpt-oss. https://openai.com/pl-PL/index/introducing-gpt-oss/
- OpenAI. (2025c). Tasks in ChatGPT. https://help.openai.com/en/articles/10291617-tasks-in-chatgpt
- OpenAI. (2026). OpenAI and Hugging Face partner to address security incident during model evaluation. https://openai.com/index/hugging-face-model-evaluation-security-incident/
- Oppenlaender, J. (2024). A taxonomy of prompt modifiers for text-to-image generation. Behaviour i Information Technology, 43(15), 3763–3776. https://doi.org/10.1080/0144929X.2023.2286532
- Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., i Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
- Pan, J., i Xu, X. (2026). Political censorship in large language models originating from China. PNAS Nexus, 5(2), pgag013. https://doi.org/10.1093/pnasnexus/pgag013
- Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., i Dean, J. (2021). Carbon Emissions and Large Neural Network Training. ArXiv. http://arxiv.org/abs/2104.10350
- Pattnayak, P., i Bohra, H. (2026). Review of Tools for Zero-Code LLM Based Application Development. W: Proceedings of World Conference on Artificial Intelligence: Advances and Applications (s. 294–312). https://doi.org/10.1007/978-3-032-13803-3_24
- Perrigo, B. (2023). Exclusive: The $2 per hour workers who made ChatGPT less toxic. Time. https://time.com/6247678/openai-chatgpt-kenya-workers/
- Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., i Miller, A. (2019). Language Models as Knowledge Bases? Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2463–2473. https://doi.org/10.18653/v1/D19-1250
- Phan, L., Gatti, A., Han, Z., Li, N., Hu, J., Zhang, H., Zhang, C. B. C., Shaaban, M., Ling, J., Shi, S., Choi, M., Agrawal, A., Chopra, A., Khoja, A., Kim, R., Ren, R., Hausenloy, J., Zhang, O., Mazeika, M., … Hendrycks, D. (2026). Humanity’s Last Exam. ArXiv. https://doi.org/10.1038/s41586-025-09962-4
- PLLuM. (2025). PLLuM — Polish Large Language Model. https://pllum.org.pl/
- Pryzant, R., Iter, D., Li, J., Lee, Y., Zhu, C., i Zeng, M. (2023). Automatic Prompt Optimization with “Gradient Descent” and Beam Search. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 7957–7968. https://doi.org/10.18653/v1/2023.emnlp-main.494
- Pu, G., Lee, M. S., Sehwag, U. M., Lee, D. J., Zhu, B., Maurya, Y., Raghavendra, M., Xue, Y., i Denton, S. M. (2026). LHAW: Controllable Underspecification for Long-Horizon Tasks. ArXiv. http://arxiv.org/abs/2602.10525
- Pyatkin, V., Malik, S., Graf, V., Ivison, H., Huang, S., Dasigi, P., Lambert, N., i Hajishirzi, H. (2025). Generalizing Verifiable Instruction Following. ArXiv. http://arxiv.org/abs/2507.02833
- Qin, Q., Zhuo, L., Xin, Y., Du, R., Li, Z., Fu, B., Lu, Y., Li, X., Liu, D., Zhu, X., Beddow, W., Millon, E., Perez, V., Wang, W., Qiao, Y., Zhang, B., Liu, X., Li, H., Xu, C., i Gao, P. (2025). Lumina-Image 2.0: A Unified and Efficient Image Generative Framework. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 20031–20042.
- QwenTeam. (2026). Qwen3.5: Accelerating Productivity with Native Multimodal Agents. https://qwen.ai/blog?id=qwen3.5
- Raghavendran, K. R., i Elragal, A. (2023). Low-Code Machine Learning Platforms: A Fastlane to Digitalization. Informatics, 10(2), 50. https://doi.org/10.3390/informatics10020050
- Raji, I. D., Denton, E., Bender, E. M., Hanna, A., i Paullada, A. (2021). AI and the Everything in the Whole Wide World Benchmark. 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
- Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., i Koltun, V. (2022). Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-Shot Cross-Dataset Transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3), 1623–1637. https://doi.org/10.1109/TPAMI.2020.3019967
- Rein, D., Hou, B. L., Stickland, A. C., Petty, J., Pang, R. Y., Dirani, J., Michael, J., i Bowman, S. R. (2023). GPQA: A Graduate-Level Google-Proof Q&A Benchmark. ArXiv. http://arxiv.org/abs/2311.12022
- Ren, Z., Zhan, Y., Yu, B., i Tao, D. (2025). Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation. ArXiv. http://arxiv.org/abs/2503.19937
- Replit. (2026). Replit. https://replit.com/
- Reynolds, L., i McDonell, K. (2021). Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 1–7. https://doi.org/10.1145/3411763.3451760
- Richardson, C., Rymer, J. R., Mines, C., Cullen, A., i Whittaker, D. (2014). New development platforms emerge for customer-facing applications.
- Romanou, A., Foroutan, N., Sotnikova, A., Chen, Z., Nelaturu, S. H., Singh, S., Maheshwary, R., Altomare, M., Haggag, M. A., Amayuelas, A., Amirudin, A. H., Aryabumi, V., Boiko, D., Chang, M., Chim, J., Cohen, G., Dalmia, A. K., Diress, A., … Bosselut, A. (2024). INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge. ArXiv. http://arxiv.org/abs/2411.19799
- Rombach, R., Blattmann, A., Lorenz, D., Esser, P., i Ommer, B. (2022). High-Resolution Image Synthesis With Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695.
- Rowan, J., Mittal, N., Ammanath, B., i Perricos, C. (2026). State of AI in the Enterprise: The untapped edge.
- Rozporządzenie Parlamentu Europejskiego i Rady. (2024). Rozporządzenie Parlamentu Europejskiego i Rady (UE) 2024/1689 z dnia 13 czerwca 2024 r. w sprawie ustanowienia zharmonizowanych przepisów dotyczących sztucznej inteligencji (Artificial Intelligence Act). Dziennik Urzędowy Unii Europejskiej, L 2024/1689.
- Rumelhart, D. E., Hinton, G. E., i Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
- Russell, S., i Norvig, P. (2023). Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Helion.
- Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., Ho, J., Fleet, D. J., i Norouzi, M. (2022). Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. Advances in Neural Information Processing Systems, 35, 36479–36494.
- Sajadieh, S., Fattorini, L., Perrault, R., Gil, Y., Parli, V., Santarlasci, L., Pava, J., Maslej, N., Altman, R., Brynjolfsson, E., Brodley, C., Clark, J., Dignum, V., Kumar, V., Landay, J., Lyons, T., Manyika, J., Niebles, J. C., Shoham, Y., … Weld, D. (2026). The AI Index 2026 Annual Report. Stanford University.
- Same.New. (2026). Same.New. https://same.new/
- Sanh, V., Webson, A., Raffel, C., Bach, S., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Raja, A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma, S. S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N., Datta, D., … Rush, A. M. (2022). Multitask Prompted Training Enables Zero-Shot Task Generalization. International Conference on Learning Representations. https://openreview.net/forum?id=9Vrb9D0WI4
- Sapkota, R., Roumeliotis, K. I., i Karkee, M. (2026). AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges. Information Fusion, 126, 103599. https://doi.org/10.1016/j.inffus.2025.103599
- Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417–424. https://doi.org/10.1017/S0140525X00005756
- Seedream, T., Chen, Y., Gao, Y., Gong, L., Guo, M., Guo, Q., Guo, Z., Hou, X., Huang, W., Huang, Y., Jian, X., Kuang, H., Lai, Z., Li, F., Li, L., Lian, X., Liao, C., Liu, L., Liu, W., … Zhu, W. (2025). Seedream 4.0: Toward Next-generation Multimodal Image Generation. ArXiv. http://arxiv.org/abs/2509.20427
- Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., i Dean, J. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ArXiv. http://arxiv.org/abs/1701.06538
- Shen, M., Li, Y., Chen, L., Fan, Z., Li, Y., i Yang, Q. (2026). From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent. http://arxiv.org/abs/2505.02024
- Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., i Singh, S. (2020). Autoprompt: Eliciting knowledge from language models with automatically generated prompts. W: B. Webber, T. Cohn, Y. He, i Y. Liu (red.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (s. 4222–4235). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.346
- Shridhar, S., i Bose, S. (2021). Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies. International Journal for Research in Applied Science i Engineering Technology, 9(12), 508–513.
- Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., i Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
- Singh, A., Ehtesham, A., Kumar, S., i Khoei, T. T. (2024). Enhancing AI Systems with Agentic Workflows Patterns in Large Language Model. 2024 IEEE World AI IoT Congress, 527–532. https://doi.org/10.1109/AIIoT61789.2024.10578990
- Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., i Ganguli, S. (2015). Deep Unsupervised Learning using Nonequilibrium Thermodynamics. Proceedings of the 32nd International Conference on Machine Learning, 37, 2256–2265.
- Sokołowski, P. (2025). NeuronWriter. W: SEO w praktyce (s. 580–588).
- South, T., Marro, S., Hardjono, T., Mahari, R., Whitney, C. D., Greenwood, D., Chan, A., i Pentland, A. (2025). Authenticated Delegation and Authorized AI Agents. ArXiv. http://arxiv.org/abs/2501.09674
- Steinberger, P. (2026). OpenClaw — Personal AI Assistant. https://openclaw.ai/
- Stelling, L., Yang, M., Gipiškis, R., Staufer, L., Chin, Z. S., Campos, S., Gil, A., i Chen, M. (2025). Mapping Industry Practices to the EU AI Act’s GPAI Code of Practice Safety and Security Measures.
- Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H., i Rush, A. M. (2022). Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation With Large Language Models. IEEE Transactions on Visualization and Computer Graphics, 29(1), 1146–1156. https://doi.org/10.1109/TVCG.2022.3209479
- Strubell, E., Ganesh, A., i McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650. https://doi.org/10.18653/v1/P19-1355
- Szpringer, W. (2026). Big techy na rynku modeli podstawowych sztucznej inteligencji. Nowe wyzwania dla polityki konkurencji. W: T. Doligalski i D. Kaszyński (red.), Sztuczna inteligencja w naukach ekonomicznych. Difin.
- Takahashi, N., Javed, A., i Kohda, Y. (2024). How Low-Code Tools Contribute to Diversity, Equity, and Inclusion (DEI) in the Workplace: A Case Study of a Large Japanese Corporation. Sustainability, 16(13), 5327. https://doi.org/10.3390/su16135327
- Terminal-Bench. (2026). Terminal-Bench 2.0. https://www.tbench.ai/benchmarks/terminal-bench-2
- Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., i Lample, G. (2023). LLaMA: Open and Efficient Foundation Language Models. ArXiv. http://arxiv.org/abs/2302.13971
- Treviso, M., Lee, J.-U., Ji, T., Aken, B. van, Cao, Q., Ciosici, M. R., Hassid, M., Heafield, K., Hooker, S., Raffel, C., Martins, P. H., Martins, A. F. T., Forde, J. Z., Milder, P., Simpson, E., Slonim, N., Dodge, J., Strubell, E., Balasubramanian, N., … Schwartz, R. (2023). Efficient Methods for Natural Language Processing: A Survey. Transactions of the Association for Computational Linguistics, 11, 826–860. https://doi.org/10.1162/tacl_a_00577
- Trigo, A., Varajao, J., i Almeida, M. (2022). Low-Code Versus Code-Based Software Development: Which Wins the Productivity Game? IT Professional, 24(5), 61–68. https://doi.org/10.1109/MITP.2022.3189880
- Troszyński, M., Berg, J., Gmyrek, P., Kamiński, K., Konopczyński, F., Ładna, A., Nafradi, B., i Rosłaniec, K. (2025). Generatywna sztuczna inteligencja a polski rynek pracy. https://www.nask.pl/media/2025/06/Generatywna-sztuczna-inteligencja-a-polski-rynek-pracy.pdf
- Turing, A. M. (1950). I.—Computing Machinery And Intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
- v0. (2026). v0. https://v0.app/
- van Berlo, Z. M. C., Campbell, C., i Voorveld, H. A. M. (2024). The MADE Framework: Best Practices for Creating Effective Experimental Stimuli Using Generative AI. Journal of Advertising, 53(5), 732–753. https://doi.org/10.1080/00913367.2024.2397777
- van der Weij, T., Hofstätter, F., Jaffe, O., Brown, S., i Ward, F. (2025). AI Sandbagging: Language Models can Strategically Underperform on Evaluations. W: Y. Yue, A. Garg, N. Peng, F. Sha, i R. Yu (red.), International Conference on Learning Representations (Vol. 2025, s. 73152–73189). https://proceedings.iclr.cc/paper_files/paper/2025/file/b5e5753b0a0e440a6d8dc7e143617cec-Paper-Conference.pdf
- Vashistha, R., Gulzar, A., Kundu, P., Sharma, P., Brunstein, M., i Vegh, V. (2025). The potential role of AI agents in transforming nuclear medicine research and cancer management in India. ArXiv. http://arxiv.org/abs/2503.07673
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., i Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, 30, 5998–6008.
- Velásquez-Henao, J. D., Franco-Cardona, C. J., i Cadavid-Higuita, L. (2023). Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA, 90(230), 9–17. https://doi.org/10.15446/dyna.v90n230.111700
- Wang, K., Pan, J., Shi, W., Lu, Z., Ren, H., Zhou, A., Zhan, M., i Li, H. (2024). Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset. Advances in Neural Information Processing Systems, 95095–95169. https://doi.org/10.52202/079017-3014
- Wang, L., Yang, N., Huang, X., Yang, L., Majumder, R., i Wei, F. (2024). Multilingual E5 Text Embeddings: A Technical Report. ArXiv. http://arxiv.org/abs/2402.05672
- Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., i Zhou, D. (2023). Self-Consistency Improves Chain of Thought Reasoning in Language Models. ArXiv. http://arxiv.org/abs/2203.11171
- Wang, Z. J., Chakravarthy, A., Munechika, D., i Chau, D. H. (2024). Wordflow: Social Prompt Engineering for Large Language Models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, 3, 42–50.
- Węcel, K., i Tym, M. (2026). Słowa, zdania czy akapity – jak badać podobieństwo i oceniać wiarygodność informacji? W: T. Doligalski i D. Kaszyński (red.), Sztuczna inteligencja w naukach ekonomicznych. Difin.
- Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., i Le, Q. V. (2022). Finetuned Language Models are Zero-Shot Learners. International Conference on Learning Representations. https://openreview.net/forum?id=gEZrGCozdqR
- Wei, J., Sun, Z., Papay, S., McKinney, S., Han, J., Fulford, I., Chung, H. W., Passos, A. T., Fedus, W., i Glaese, A. (2025). BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents. ArXiv. http://arxiv.org/abs/2504.12516
- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V, i Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. Advances in Neural Information Processing Systems, 35, 24824–24837.
- Wen, B., Ke, P., Sun, Y., Wang, C., Gu, X., Zhou, J., Tang, J., Wang, H., i Huang, M. (2025). HPSS: Heuristic Prompting Strategy Search for LLM Evaluators. Findings of the Association for Computational Linguistics: ACL 2025, 24974–25007. https://doi.org/10.18653/v1/2025.findings-acl.1282
- Wong, R., Wang, J., Zhao, J., Chen, L., Gao, Y., Zhang, L., Zhou, X., Wang, Z., Xiang, K., Zhang, G., Huang, W., Wang, Y., i Wang, K. (2025). WideSearch: Benchmarking Agentic Broad Info-Seeking. ArXiv. http://arxiv.org/abs/2508.07999
- World Bank. (2025). Digital Progress and Trends Report 2025: Strengthening AI Foundations. World Bank, Washington, DC. https://doi.org/10.1596/978-1-4648-2264-3
- Wu, C., Li, J., Zhou, J., Lin, J., Gao, K., Yan, K., Yin, S., Bai, S., Xu, X., Chen, Y., Chen, Y., Tang, Z., Zhang, Z., Wang, Z., Yang, A., Yu, B., Cheng, C., Liu, D., Li, D., … Liu, Z. (2025). Qwen-Image Technical Report. ArXiv. http://arxiv.org/abs/2508.02324
- Wu, P., i Xie, S. (2024). V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13084–13094.
- Wu, T., Jiang, E., Donsbach, A., Gray, J., Molina, A., Terry, M., i Cai, C. J. (2022). PromptChainer: Chaining Large Language Model Prompts through Visual Programming. CHI Conference on Human Factors in Computing Systems Extended Abstracts, 1–10. https://doi.org/10.1145/3491101.3519729
- Wu, T., Lan, J., Yuan, W., Jiao, J., Weston, J., i Sukhbaatar, S. (2024). Thinking LLMs: General Instruction Following with Thought Generation. ArXiv. http://arxiv.org/abs/2410.10630
- Wu, T., Terry, M., i Cai, C. J. (2022). AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts. CHI Conference on Human Factors in Computing Systems, 1–22. https://doi.org/10.1145/3491102.3517582
- Wu, Z., Liu, X., Zhang, X., Chen, L., Meng, F., Du, L., Zhao, Y., Zhang, F., Ye, Y., Wang, J., Wang, Z., Ni, J., Yang, Y., Xu, A., i Shieh, M. Q. (2025). MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use. ArXiv. http://arxiv.org/abs/2509.24002
- Xu, R., i Peng, J. (2025). A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications. ArXiv. http://arxiv.org/abs/2506.12594
- Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V, Zhou, D., i Chen, X. (2024). Large Language Models as Optimizers. The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=Bb4VGOWELI
- Yang, D., Liu, S., Huang, R., Weng, C., i Meng, H. (2024). InstructTTS: Modelling Expressive TTS in Discrete Latent Space With Natural Language Style Prompt. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 32, 2913–2925. https://doi.org/10.1109/TASLP.2024.3402088
- Yang, D., Yu, J., Wang, H., Wang, W., Weng, C., Zou, Y., i Yu, D. (2023). Diffsound: Discrete Diffusion Model for Text-to-Sound Generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1720–1733. https://doi.org/10.1109/TASLP.2023.3268730
- Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., i Narasimhan, K. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Advances in Neural Information Processing Systems, 36, 11809–11822.
- Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K. R., i Cao, Y. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=WE_vluYUL-X
- Ye, B., Li, R., Yang, Q., Liu, Y., Yao, L., Lv, H., Xie, Z., An, C., Li, L., Kong, L., Liu, Q., Sui, Z., i Yang, T. (2026). Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents. ArXiv. http://arxiv.org/abs/2604.06132
- Z.ai. (2025). GLM-4.7: Advancing the Coding Capability. https://z.ai/blog/glm-4.7
- Zan, D., Huang, Z., Liu, W., Chen, H., Zhang, L., Xin, S., Chen, L., Liu, Q., Zhong, X., Li, A., Liu, S., Xiao, Y., Chen, L., Zhang, Y., Su, J., Liu, T., Long, R., Shen, K., i Xiang, L. (2025). Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving. ArXiv. http://arxiv.org/abs/2504.02605
- Zha, D., Bhat, Z. P., Lai, K.-H., Yang, F., Jiang, Z., Zhong, S., i Hu, X. (2025). Data-centric Artificial Intelligence: A Survey. ACM Computing Surveys, 57(5), 1–42. https://doi.org/10.1145/3711118
- Zhang, J., Yang, K., Wan, Y., Ye, M., Yang, B., Huang, F., Lin, J., i Liu, D. (2025). NOVA-63: Native Omni-lingual Versatile Assessments of 63 Disciplines. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 7158–7200. https://doi.org/10.18653/v1/2025.emnlp-main.364
- Zhang, L., Rao, A., i Agrawala, M. (2023). Adding Conditional Control to Text-to-Image Diffusion Models. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 3836–3847.
- Zhang, Y. (2026). Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists? ArXiv. http://arxiv.org/abs/2602.22401
- Zhang, Y., Jiang, S., Li, R., Tu, J., Su, Y., Deng, L., Guo, X., Lv, C., i Lin, J. (2026). DeepPlanning: Benchmarking Long-Horizon Agentic Planning with Verifiable Constraints. ArXiv. http://arxiv.org/abs/2601.18137
- Zhang, Y., Yang, X., Xu, X., Gao, Z., Huang, Y., Mu, S., Feng, S., Wang, D., Zhang, Y., Song, K., i Yu, G. (2026). Affective computing in the era of large language models: A survey from the NLP perspective. Knowledge-Based Systems, 337, 115411. https://doi.org/10.1016/j.knosys.2026.115411
- Zhang, Z., Chen, X., Yang, F., Qin, X., Du, C., Cheng, X., Liu, H., Lin, Q., Rajmohan, S., i Zhang, D. (2025). AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure. ArXiv. http://arxiv.org/abs/2409.17642
- Zhang, Z., i Saber, T. (2024). Assessing the Code Clone Detection Capability of Large Language Models. 2024 4th International Conference on Code Quality, 75–83. https://doi.org/10.1109/ICCQ60895.2024.10576803
- Zhou, J., Lu, T., Mishra, S., Brahma, S., Basu, S., Luan, Y., Zhou, D., i Hou, L. (2023). Instruction-Following Evaluation for Large Language Models. ArXiv. http://arxiv.org/abs/2311.07911
- Zhu, M., Tian, M., Yang, X., Zhou, T., Yuan, L., Zhu, P., Chertkov, E., Liu, S., Du, Y., Ji, Z., Das, I., Cao, J., Du, Y., Yu, J., Wu, P., He, J., Su, Y., Jiang, Y., Zhang, Y., … Peng, H. (2025). Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark. ArXiv. http://arxiv.org/abs/2509.26574