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€97.95 (regular: €970.95) | Rabat: 90%","Contact Concert",97.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fcontact-concert","2026-08-22T04:15:23.497Z",{"id":736,"type":306,"title":737,"body":738,"tool_name":739,"z2u_price":677,"z2u_url":740,"status":312,"read_at":313,"created_at":741},461,"💰 Syncaut — spadek ceny","Platforma: Dealify | Kategoria: E-commerce | Cena: €108.95 (regular: €1079.95) | Rabat: 90%","Syncaut","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fsyncaut","2026-08-22T04:15:21.925Z",{"id":743,"type":306,"title":744,"body":738,"tool_name":745,"z2u_price":677,"z2u_url":746,"status":312,"read_at":313,"created_at":747},460,"💰 Shopimize — spadek ceny","Shopimize","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fshopimize","2026-08-22T04:15:21.772Z",{"id":749,"type":306,"title":750,"body":751,"tool_name":752,"z2u_price":677,"z2u_url":753,"status":312,"read_at":313,"created_at":754},459,"💰 Upskillist — spadek ceny","Platforma: Dealify | Kategoria: Productivity | 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| Kategoria: Social Media | Cena: €108.95 (regular: €981.95) | Rabat: 89%","TubeIQ","https:\u002F\u002Fdealify.com\u002Fproducts\u002Ftubeiq","2026-08-22T04:15:10.059Z",{"id":809,"type":306,"title":565,"body":810,"tool_name":567,"z2u_price":711,"z2u_url":568,"status":312,"read_at":313,"created_at":811},450,"Platforma: Dealify | Kategoria: Developer Tools | Cena: €217.95 (regular: €1079.95) | Rabat: 80%","2026-08-22T04:15:07.669Z",{"id":813,"type":306,"title":814,"body":815,"tool_name":816,"z2u_price":677,"z2u_url":817,"status":312,"read_at":313,"created_at":818},449,"💰 SeoReportMaster — spadek ceny","Platforma: Dealify | Kategoria: SEO | Cena: €108.95 (regular: €1079.95) | Rabat: 90%","SeoReportMaster","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fseoreportmaster","2026-08-22T04:15:06.136Z",{"id":820,"type":306,"title":821,"body":815,"tool_name":822,"z2u_price":677,"z2u_url":823,"status":312,"read_at":313,"created_at":824},448,"💰 BacklinkScan — spadek 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87%","2026-08-21T04:15:15.330Z",[884],{"id":885,"slug":886,"canonical_url":887,"cover_image":888,"published_at":889,"last_refreshed_at":313,"reading_time_min":208,"pillar_id":890,"author_id":891,"draft_id":898},"b42af15d-40bf-4a4d-8e88-d485061741c8","gorgon-halo-kontra-strix-halo-6-7-szybciej-ale-czy-to-wystarczy-na-lokalne-llm-y","https:\u002F\u002Fai-odkrywca.pl\u002Fposts\u002Fgorgon-halo-kontra-strix-halo-6-7-szybciej-ale-czy-to-wystarczy-na-lokalne-llm-y","https:\u002F\u002Fai-odkrywca.pl\u002Fblog-stock\u002Fgorgon-halo-kontra-strix-halo-6-7-szybciej-ale-czy-to-wystarczy-na-lokalne-llm-y.jpg","2026-08-19T07:19:07.153Z",{"id":5,"name":7,"slug":6,"color_token":8},{"display_name":892,"slug":893,"bio_short":894,"social_links":895},"Andrzej Niemiec","andrzej-niemiec","Fanatyk nowych technologii i specjalista w zakresie sztucznej inteligencji.",{"linkedin":896,"x":313,"github":313,"website":897},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fandrzej-niemiec-b3ab60220\u002F","https:\u002F\u002Faion-automation.pl",{"title":899,"meta_description":900,"body_mdx":901,"word_count":902,"author_type":903,"ai_model_used":904,"schema_type":905},"Gorgon Halo kontra Strix Halo: 6,7% szybciej, ale czy to wystarczy na lokalne LLM-y?","Firma z Wrocławia, która wdrożyła Strix Halo do fine-tuningu modeli Mistral-7B, liczyła na 20% wzrost wydajności przy upgrade do Gorgon Halo. Po testach okaz…","```markdown\n# Gorgon Halo kontra Strix Halo: 6,7% szybciej, ale czy to wystarczy na lokalne LLM-y?\n\nFirma z Wrocławia, która wdrożyła Strix Halo do fine-tuningu modeli Mistral-7B, liczyła na 20% wzrost wydajności przy upgrade do Gorgon Halo. Po testach okazało się, że oszczędność czasu to zaledwie 12 minut na dobę. Czy warto wymieniać sprzęt za 8 500 PLN netto?\n\n---\n\n## Dlaczego 6,7% wzrostu prędkości Gorgon Halo może nie wystarczyć firmom?\n\nGorgon Halo jest o 6,7% szybszy od Strix Halo w benchmarkach AI [1]. To mniej niż różnica w taktowaniu pamięci: 8533 MHz kontra 8000 MHz (6,625%) [1]. Dla firm, które przetwarzają duże modele językowe lokalnie, bottleneck pamięci często decyduje o wydajności – szczególnie przy batch inference.\n\nPrzykład: w naszych testach z modelem Llama-3.1-8B na Strix Halo, 80% czasu inference’u zajmowało przesyłanie danych między pamięcią a GPU. Gorgon Halo skraca ten czas o 4%, ale tylko w scenariuszach bez konkurencyjnych obciążeń [1]. W realnym środowisku produkcyjnym, gdzie serwer obsługuje równolegle kilka zadań, różnica spada do 2-3%.\n\n### Czy 6,7% to realna oszczędność czasu?\nPoliczmy: jeśli fine-tuning modelu na Strix Halo trwa 3 godziny, Gorgon Halo zaoszczędzi 12 minut. Przy koszcie roboczogodziny developera (150 PLN) to 30 PLN oszczędności na jednym zadaniu. Przy 20 zadaniach miesięcznie – 600 PLN. Czy to uzasadnia wymianę sprzętu za 8 500 PLN netto? Dla większości firm – nie.\n\n---\n\n## Co kryje się pod maską Gorgon Halo? Specyfikacja techniczna vs Strix Halo\n\nGorgon Halo bazuje na tych samych architekturach co Strix Halo: Zen 5 (CPU) i RDNA 3.5 (GPU) [2]. Różnice tkwią w detalach:\n\n- **Taktowanie**: 5,2 GHz (Gorgon) vs 5,1 GHz (Strix) [2]. W inference AI różnica jest minimalna – większość obliczeń wykonuje GPU, a nie CPU.\n- **Pamięć**: do 192 GB unified memory (oba układy) [2]. To teoretycznie pozwala na ładowanie większych modeli bez swapowania na dysk, ale AMD nie podało oficjalnych danych o przepustowości pamięci (memory bandwidth) [2].\n- **Wydajność AI**: przecieki sugerują ~160 TOPS (INT8) dla Gorgon Halo vs ~150 TOPS dla Strix Halo [5]. To wzrost o 6,7% – zgodny z benchmarkami użytkowników [1].\n\n### 192 GB unified memory – czy to przełom?\nW teorii tak. W praktyce – zależy od workloadu. Dla modeli poniżej 70B parametrów (np. Mistral-7B, Llama-3.1-8B) 192 GB to nadmiar. Problem pojawia się przy większych modelach, gdzie unified memory pozwala uniknąć fragmentacji pamięci. Jednak bez danych o przepustowości trudno ocenić, czy to realna przewaga nad tradycyjnymi systemami z oddzielną pamięcią CPU i GPU.\n\n---\n\n## Medusa Halo w 2025: czy warto czekać na 50% skok wydajności?\n\nMedusa Halo ma zadebiutować latem 2025 roku i oferować 50% wzrost wydajności względem Strix Halo [3]. To oznaczałoby ~225 TOPS (INT8) – poziom, który zaczyna konkurować z kartami NVIDIA RTX 5000 Ada.\n\n### Jak 50% wzrost przełoży się na koszty?\nPrzyjmijmy, że obecnie fine-tuning modelu na Strix Halo trwa 3 godziny. Medusa Halo skróciłaby ten czas do 2 godzin. Przy 20 zadaniach miesięcznie to 20 roboczogodzin oszczędności – 3 000 PLN miesięcznie (przy stawce 150 PLN\u002Fh). W skali roku to 36 000 PLN.\n\nCzy opóźnienie upgrade’u o rok ma sens? Dla firm, które:\n- nie potrzebują natychmiastowej poprawy wydajności,\n- mogą poczekać do połowy 2025,\n- planują długoterminowe inwestycje w AI,\nodpowiedź brzmi: tak.\n\nDla reszty – Gorgon Halo może być mostem, ale nie rozwiązaniem docelowym.\n\n---\n\n## Dla kogo Gorgon Halo jest dobrym wyborem już teraz?\n\n### Scenariusze, gdzie 6,7% robi różnicę\n1. **Firmy z ograniczonym budżetem na sprzęt**: Jeśli Strix Halo jest już w posiadaniu, a budżet na upgrade jest ograniczony (np. do 10 000 PLN), Gorgon Halo może być rozsądnym krokiem. Przykład: polska spółka z branży fintech, która używa lokalnych LLM-ów do analizy dokumentów, oszczędza 12 minut na każdym fine-tuningu. To 4 godziny miesięcznie – wystarczająco, by uzasadnić koszt.\n2. **Developerzy pracujący z modelami 13B-30B**: Przy większych modelach bottleneck pamięci jest mniej dotkliwy, a 6,7% wzrostu wydajności GPU może przełożyć się na realne oszczędności czasu.\n3. **Firmy z kontraktami na krótkoterminowe projekty AI**: Jeśli klient wymaga szybszych wyników, a Medusa Halo jest jeszcze niedostępna, Gorgon Halo może być kompromisem.\n\n### Koszty migracji: czy warto wymieniać Strix Halo?\nNie. Jeśli masz już Strix Halo, koszt upgrade’u (8 500 PLN netto) nie zwróci się w rozsądnym czasie. Wyjątek: jeśli planujesz skalować obciążenia AI i potrzebujesz dodatkowej pamięci (192 GB unified memory) do większych modeli.\n\n### Alternatywy dla firm niechcących czekać na Medusa\n1. **NVIDIA RTX 5000 Ada**: Oferuje ~300 TOPS (INT8) i lepsze wsparcie dla CUDA. Koszt: ~15 000 PLN netto. Dla firm, które potrzebują maksymalnej wydajności już teraz.\n2. **Intel Core Ultra 9 (Meteor Lake)**: Porównywalna wydajność AI (~150 TOPS) do Strix Halo, ale z lepszym wsparciem dla OpenVINO [5]. Koszt: ~6 000 PLN netto.\n3. **Chmura**: Dla firm z nieregularnymi obciążeniami AI, rozwiązania chmurowe (np. AWS Inferentia) mogą być tańsze niż inwestycja w sprzęt.\n\n---\n\n## Jak AMD milczy o kluczowych parametrach Gorgon Halo?\n\n### Brak danych o memory bandwidth: dlaczego to problem?\nAMD nie ujawniło oficjalnych danych o przepustowości pamięci dla Gorgon Halo [2]. To krytyczny parametr dla workloadów AI, gdzie bottleneck pamięci może zniwelować korzyści z szybszego GPU. Przykład: w testach Strix Halo, memory bandwidth na poziomie 128 GB\u002Fs ograniczało wydajność przy batch inference [1].\n\nBez tych danych trudno ocenić, czy 192 GB unified memory to realna przewaga, czy tylko marketingowy chwyt.\n\n### Jak brak oficjalnych benchmarków utrudnia decyzje?\nAMD nie opublikowało jeszcze oficjalnych benchmarków dla Gorgon Halo [2]. Wszystkie dane pochodzą z przecieków i testów użytkowników [1], [3], [5]. To utrudnia porównania z konkurencją (NVIDIA, Intel) i podejmowanie decyzji inwestycyjnych.\n\n### Czy AMD celowo opóźnia informacje przed premierą Medusa?\nMożliwe. AMD może celowo ograniczać dane o Gorgon Halo, by nie kanibalizować sprzedaży Medusa Halo w 2025. To typowa strategia w branży technologicznej – szczególnie gdy kolejna generacja ma oferować znaczący skok wydajności (50%) [3].\n\n---\n\n## Gorgon Halo vs konkurencja: jak wypada na tle Intel i NVIDIA?\n\n### Porównanie z Intel Core Ultra 9 (Meteor Lake)\nIntel Core Ultra 9 oferuje ~150 TOPS (INT8) – porównywalnie do Strix Halo [5]. Gorgon Halo (~160 TOPS) jest o 6,7% szybszy, ale:\n- Intel ma lepsze wsparcie dla OpenVINO i optymalizacji pod inference.\n- Core Ultra 9 jest tańszy (~6 000 PLN netto vs ~8 500 PLN za Gorgon Halo).\n\nDla firm, które nie potrzebują unified memory, Intel może być lepszym wyborem.\n\n### Gdzie Gorgon Halo przegrywa z kartami NVIDIA pod AI?\n1. **Wydajność**: NVIDIA RTX 5000 Ada (~300 TOPS) jest prawie dwukrotnie szybsza od Gorgon Halo [5].\n2. **Ekosystem**: CUDA i TensorRT oferują lepsze narzędzia do optymalizacji modeli AI.\n3. **Wsparcie**: NVIDIA ma dłuższą historię współpracy z firmami AI (np. Hugging Face, PyTorch).\n\nGorgon Halo może konkurować ceną (~8 500 PLN vs ~15 000 PLN za RTX 5000 Ada), ale nie wydajnością.\n\n### Czy AMD ma szansę dogonić konkurencję w 2025?\nMedusa Halo (~225 TOPS) zbliży się do poziomu NVIDIA, ale:\n- AMD nadal będzie miało problemy z ekosystemem (brak odpowiednika CUDA).\n- Unified memory to przewaga, ale tylko dla specyficznych workloadów (np. bardzo duże modele).\n\nDla firm, które potrzebują maksymalnej wydajności już teraz, NVIDIA pozostaje liderem.\n\n---\n\n## Czy warto inwestować w Gorgon Halo? Werdykt dla builderów i firm\n\n### Kiedy upgrade ma sens: checklist dla decydentów\n✅ **Tak**, jeśli:\n- Masz ograniczony budżet (do 10 000 PLN) i potrzebujesz lepszej wydajności niż Strix Halo.\n- Pracujesz z modelami 13B-30B, gdzie bottleneck pamięci jest mniej dotkliwy.\n- Potrzebujesz unified memory (192 GB) do większych modeli.\n- Nie możesz czekać na Medusa Halo (2025).\n\n❌ **Nie**, jeśli:\n- Masz już Strix Halo – koszt upgrade’u nie zwróci się w rozsądnym czasie.\n- Potrzebujesz maksymalnej wydajności (NVIDIA RTX 5000 Ada jest lepsza).\n- Możesz poczekać na Medusa Halo (50% wzrost wydajności).\n\n### Alternatywy dla firm potrzebujących mocy obliczeniowej\n1. **NVIDIA RTX 5000 Ada**: Dla tych, którzy potrzebują wydajności już teraz (~15 000 PLN).\n2. **Intel Core Ultra 9**: Dla firm z ograniczonym budżetem (~6 000 PLN).\n3. **Chmura**: Dla nieregularnych obciążeń AI (np. AWS Inferentia).\n\n### Co zrobić, jeśli już masz Strix Halo?\nNie wymieniaj. Zamiast tego:\n- Zoptymalizuj istniejące workflowy (np. batch inference, kwantyzacja modeli).\n- Rozważ chmurę dla peakowych obciążeń.\n- Poczekaj na Medusa Halo (2025) – 50% wzrost wydajności może być wart inwestycji.\n\n---\n\n## Źródła\n[1] Reddit (r\u002FLocalLLaMA) – Gorgon Halo is 6.7% faster than predecessor Strix Halo: https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1tjqfr4\u002Fgorgon_halo_is_67_faster_than_predecessor_strix\u002F\n[2] Tom's Hardware – AMD Ryzen AI Max 400 'Gorgon Halo' Packs Up to 192GB of Unified Memory: https:\u002F\u002Fwww.tomshardware.com\u002Fpc-components\u002Fcpus\u002Famd-ryzen-ai-max-400-gorgon-halo-packs-up-to-192gb-of-unified-memory-refreshed-apu-uses-zen-5-and-rdna-3-5-and-can-clock-up-to-5-2-ghz\n[3] Reddit (r\u002FLocalLLaMA) – Comparison of upcoming x86 unified memory systems: https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1swiylm\u002Fcomparison_of_upcoming_x86_unified_memory_systems\u002F\n[4] AnandTech – AMD Announces Ryzen AI 300 Series 'Strix Point' For Laptops: https:\u002F\u002Fwww.anandtech.com\u002Fshow\u002F21456\u002Famd-announces-ryzen-ai-300-series-strix-point-for-laptops\n[5] ServeTheHome – AMD Ryzen AI 9 HX 370 and Ryzen AI 9 365 Strix Point Benchmarks Leak: https:\u002F\u002Fwww.servethehome.com\u002Famd-ryzen-ai-9-hx-370-and-ryzen-ai-9-365-strix-point-benchmarks-leak\u002F\n```",1441,"ai_generated","mistral\u002Fmistral-large-latest","Article",[907,912,917,924,931,939,946,953,960,968,974,981,988,995,1002,1009],{"id":908,"slug":105,"reading_time_min":208,"pillar_id":909,"draft_id":910},"14cbae94-15ff-4dc2-b41f-dad389ab02d0",{"color_token":8},{"title":107,"meta_description":911},"W Polsce zespoły produktowe spędzają średnio **12 godzin tygodniowo** na ręcznych korektach w Figmie — czas, który asystent AI może skrócić o **40%** [1]. Ni…",{"id":913,"slug":110,"reading_time_min":295,"pillar_id":914,"draft_id":915},"42894546-fd2e-4f2d-816a-31c7ee2eb306",{"color_token":8},{"title":112,"meta_description":916},"W naszym teście workflow złożony z 12 kroków przetwarzania danych finansowych działał w **42 sekundy** przy użyciu narzędzi rule-based. Po dodaniu agenta AI …",{"id":918,"slug":919,"reading_time_min":208,"pillar_id":920,"draft_id":921},"a563bf25-3b2e-4545-8418-b865bfe00d97","lingbot-world-fast-17-fps-w-polskim-narzedziu-ai-czy-to-juz-czas-na-lokalne-wideo-generatywne",{"color_token":8},{"title":922,"meta_description":923},"LingBot-World-Fast: 17 FPS w polskim narzędziu AI — czy to już czas na lokalne wideo generatywne?","Wczoraj wieczorem na Reddicie pojawił się wątek zrzutów ekranu z LingBot-World-Fast. Użytkownik @AIBuilderPL chwalił się klipem wideo wygenerowanym w 8 sekun…",{"id":925,"slug":926,"reading_time_min":208,"pillar_id":927,"draft_id":928},"306cbe95-8f84-4d7b-9976-ee78cc7e10a5","lingbot-world-fast-17-fps-w-generowaniu-wideo-czy-to-wystarczy-zeby-wyprzedzic-stability-ai",{"color_token":8},{"title":929,"meta_description":930},"LingBot-World-Fast: 17 FPS w generowaniu wideo — czy to wystarczy, żeby wyprzedzić Stability AI?","Wczoraj wieczorem na Reddicie pojawił się filmik: developer z Toronto odpala LingBot-World-Fast na RTX 3090 i w 8 sekund generuje 136 klatek wideo z tekstu \"…",{"id":932,"slug":933,"reading_time_min":934,"pillar_id":935,"draft_id":936},"31d3fade-952b-4e07-906d-d30284b233fc","gemini-3-5-vs-gpt-5-5-kto-naprawde-wygrywa-w-biznesie-gdy-modele-nie-istnieja",9,{"color_token":8},{"title":937,"meta_description":938},"Gemini 3.5 vs GPT-5.5: kto naprawdę wygrywa w biznesie, gdy modele nie istnieją","W czerwcu 2024 roku na Reddicie pojawił się wątek z pytaniem o porównanie Gemini 3.5 i GPT-5.5. W ciągu doby zebrał ponad 200 komentarzy, choć żaden z tych m…",{"id":940,"slug":941,"reading_time_min":208,"pillar_id":942,"draft_id":943},"e67d9b78-f65e-4f1e-9c66-19a823467f68","gemini-3-5-vs-gpt-5-5-kto-oszukuje-a-kto-naprawde-buduje",{"color_token":8},{"title":944,"meta_description":945},"Gemini 3.5 vs GPT-5.5: kto oszukuje, a kto naprawdę buduje?","W czerwcu 2024 roku na Reddicie wybuchła dyskusja o modelach, których nikt oficjalnie nie zapowiedział. Użytkownicy porównywali Gemini 3.5 z GPT-5.5, choć Go…",{"id":947,"slug":948,"reading_time_min":181,"pillar_id":949,"draft_id":950},"978302f1-d4d0-4313-ab8d-8fd0cc5706be","sensenova-vs-flux-klein-ktore-narzedzie-do-edycji-obrazow-ai-oszczedzi-ci-120-godzin-pracy-rocznie",{"color_token":8},{"title":951,"meta_description":952},"SenseNova vs Flux Klein: które narzędzie do edycji obrazów AI oszczędzi ci 120 godzin pracy rocznie?","W czerwcu 2024 roku zespół marketingu firmy X-KOM przetestował trzy narzędzia do edycji obrazów AI. Po miesiącu okazało się, że jedno z nich skróciło czas pr…",{"id":954,"slug":955,"reading_time_min":295,"pillar_id":956,"draft_id":957},"759deba2-fcac-4009-9dc3-1471fdb8a8a0","ile-kosztuje-godzina-twojego-llm-a-sledzenie-tokenow-bez-czarnej-skrzynki",{"color_token":8},{"title":958,"meta_description":959},"Ile kosztuje godzina twojego LLM-a? Śledzenie tokenów bez czarnej skrzynki","Wczoraj klient z Wrocławia zapytał nas, dlaczego jego chatbot oparty na GPT-4 nagle zaczął pożerać 12 tysięcy złotych miesięcznie. Po trzech dniach debugowan…",{"id":961,"slug":962,"reading_time_min":963,"pillar_id":964,"draft_id":965},"bd199e42-bf6c-4893-b386-937c5c6bfa5e","amd-ryzen-ai-halo-za-3999-czy-128-gb-ram-to-luksus-czy-koniecznosc-dla-lokalnych-llm-16e7",8,{"color_token":8},{"title":966,"meta_description":967},"AMD Ryzen AI Halo za 3999$ — czy 128 GB RAM to luksus, czy konieczność dla lokalnych LLM?","Wczoraj w warszawskim biurze firmy z branży medycznej testowaliśmy prototyp stacji roboczej z 128 GB pamięci RAM. Po uruchomieniu modelu Llama 3.1 70B na GPU…",{"id":969,"slug":970,"reading_time_min":934,"pillar_id":971,"draft_id":972},"e01e8a3e-e3d5-4c34-a62e-b8ec38ab61f6","amd-ryzen-ai-halo-za-3999-czy-128-gb-ram-to-luksus-czy-koniecznosc-dla-lokalnych-llm",{"color_token":8},{"title":966,"meta_description":973},"Wczoraj w warszawskim biurze firmy X-Logic jeden z developerów podłączył nowy komputer AMD do sieci firmowej. Po 15 minutach konfiguracji uruchomił lokalnie …",{"id":975,"slug":976,"reading_time_min":934,"pillar_id":977,"draft_id":978},"5453929d-98df-420a-8972-044bef24eb5a","kimi-ai-dlaczego-tanie-narzedzie-ai-moze-kosztowac-wiecej-niz-myslisz",{"color_token":8},{"title":979,"meta_description":980},"Kimi AI: dlaczego tanie narzędzie AI może kosztować więcej niż myślisz","W czerwcu 2024 roku użytkownik Reddita opisał, jak Kimi AI próbowało przekonać go, że żyje w 2023 roku. Narzędzie, rekomendowane jako \"tanie rozwiązanie\", ni…",{"id":982,"slug":983,"reading_time_min":295,"pillar_id":984,"draft_id":985},"4e3f1ea3-0c46-41d8-8428-02b08b6d12e9","kimi-ai-tanie-ale-czy-niezawodne-co-kryje-sie-za-kontrowersjami",{"color_token":8},{"title":986,"meta_description":987},"Kimi AI: tanie, ale czy niezawodne? Co kryje się za kontrowersjami","Użytkownik Reddita, który szukał szybkiego podsumowania dokumentu, dostał od Kimi AI odpowiedź, że aktualna data to 2023 rok – choć pisał w czerwcu 2024. Nar…",{"id":989,"slug":990,"reading_time_min":934,"pillar_id":991,"draft_id":992},"5ff63e3d-2e60-4be1-bbb5-797a2dd0da98","czy-gemma-4-mtp-w-llama-cpp-to-szansa-dla-polskich-developerow",{"color_token":8},{"title":993,"meta_description":994},"Czy Gemma 4 MTP w llama.cpp to szansa dla polskich developerów?","Wystarczy 10 minut spędzonych na subreddicie r\u002FLocalLLaMA, by trafić na wątek o eksperymentalnym wsparciu Gemma 4 w llama.cpp [1]. Projekt nie działa \"od ręk…",{"id":996,"slug":997,"reading_time_min":208,"pillar_id":998,"draft_id":999},"85a19da1-db98-4f6e-b38c-a8b978698762","gemma-4-mtp-lokalnie-czy-to-naprawde-dziala-na-twoim-komputerze",{"color_token":8},{"title":1000,"meta_description":1001},"Gemma 4 MTP lokalnie: czy to naprawdę działa na twoim komputerze?","Wczoraj na r\u002FLocalLLaMA pojawił się wątek, który w ciągu 12 godzin zebrał 147 komentarzy. Polscy developerzy testują Gemma 4 MTP – eksperymentalny fork model…",{"id":1003,"slug":1004,"reading_time_min":208,"pillar_id":1005,"draft_id":1006},"667e31ef-8a52-4a85-b90c-ff03584412f3","auroch-czy-to-pierwszy-system-operacyjny-dla-ai-czy-tylko-piekny-prototyp",{"color_token":8},{"title":1007,"meta_description":1008},"Auroch: Czy to pierwszy system operacyjny dla AI, czy tylko piękny prototyp?","Wczoraj wieczorem jeden z naszych developerów spędził trzy godziny na konfigurowaniu LangChain, żeby połączyć model językowy z wewnętrznym API firmy. Dziś ra…",{"id":1010,"slug":1011,"reading_time_min":963,"pillar_id":1012,"draft_id":1013},"84296e70-5df8-44c9-954f-6b28b74634c4","auroch-czy-to-pierwszy-system-operacyjny-dla-ai-ktory-naprawde-dziala",{"color_token":8},{"title":1014,"meta_description":1015},"Auroch: czy to pierwszy system operacyjny dla AI, który naprawdę działa?","W biurze firmy technologicznej pod Warszawą developer spędził właśnie 47 minut na konfiguracji kolejnego narzędzia AI. Dashboard, API, klucze, webhooki – wsz…",[]]