Delphi13入れてみたから、Smart Codeinsight試そうかとOllamaでGemma4入れてみたけど、なぜかIDEとOllamaが通信してくれないよ😩。 原因が分からないねぇ。Ollamaのバージョンの問題なのかなぁ。

InterBase 15 Update 1 is now available 🚀 Meet the first AI-enhanced IBConsole with Smart CodeInsight for AI-powered SQL generation, optimization, query explanations, and more. Plus: UX upgrades, stability improvements, and support for Windows Server 2025 & Ubuntu 24. Learn more: 👉 tinyurl.com/27u2885t #InterBase #Databases #DevTools

Beyond CodeInsight, are there good open datasets of coding problems not limited to algorithms but also requiring usage of standard and common external libraries and tools?

【2025/12/29 生成AIニュースまとめ】 ①サマリ 生成AIが「便利な生成」から「組織の設計・統制（ガバナンス）」「運用インフラ」「顧客接点の新UI」へ広がっていることが目立った1日でした。経営層・IT部門では“AI前提”で役割やルールを作り直す議論が進み、現場ではCopilot等の定着を後押しする実務ノウハウが再注目。一方で、AI活用が進むほどルール逸脱や安全面の課題が顕在化し、リスクを前提にした体制づくりが重要になっています。さらに、アバター/動画など表現領域は「作る」より「運用して届ける」段階へ。生成AIは、性能の話だけではなく“組織と社会の作法”を変えるフェーズに入っている。 ニュース詳細 ──────────────────────── 1️⃣【生成AI×アバター/実装】AIアバターを「誰でも・簡単に」実装しやすくするため、ビジュアルコミュニケーション基盤にAIアバターモジュールを組み込む方針を発表（接客/営業/CS/教育など“運用前提”の論点） prtimes.jp/main/html/rd/p/00… 2️⃣【生成AI×IT部門/ガバナンス】IT部門は“AI前提世界”で何を担うのか、2026年の注目テーマとして整理（AI導入ではなく、組織・設計の再構築が焦点） itmedia.co.jp/enterprise/art… 3️⃣【生成AI×規程/シャドーAI】経営幹部の6割以上がAIガイドラインを無視（未承認AIの利用が情報漏えい要因に）という海外調査紹介。ルール“策定”だけでなく“守らせる設計”が論点に kn.itmedia.co.jp/kn/articles… 4️⃣【Copilot/業務ノウハウ】年末年始の“まとめ読み”として、Copilot関数など業務で効く生成AI活用ノウハウを再掲（現場の定着＝小さな成功体験の積み上げ） itmedia.co.jp/business/artic… 5️⃣【動画生成AI/社会受容】動画生成AIが物議…という文脈で、2025年の生成AIニュースを振り返る連載（“使える”と“受け入れられる”のギャップがテーマ化） itmedia.co.jp/aiplus/article… 6️⃣【AIインフラ/市場総括】2025年は「AIインフラが社会実装の基盤として定着した元年」という整理（電力・熱・半導体・サーバー等、物理制約を含めた投資競争へ） blogs.itmedia.co.jp/business… 7️⃣【AIトレンド/総括】2025年のAI新語（推論、GEO、バイブコーディング等）を総ざらいして“何が重要概念になったか”を整理（現場の言語が変わる＝実装の前提が変わる） technologyreview.jp/s/374768… 8️⃣【GPT/安全】OpenAIが「Head of Preparedness（備えの責任者）」を募集（AIの急速な進展に伴うリスク評価・脅威モデル・安全パイプライン整備を担う） businessinsider.com/openai-h… 9️⃣【Claude/開発ツール】RAD StudioのSmart CodeInsightでClaude API統合のアップデートパッチ（対応モデル拡充）。開発ツール側でも“Claude前提”の統合が進む blogs.embarcadero.com/ja/rad… Embarcadero Blog blogs.embarcadero.com ────────────────────────

🚀 New patch alert! Embarcadero has updated Smart CodeInsight to support the latest Claude models. Install now via GetIt and keep your AI suggestions running smoothly. Read more: tinyurl.com/5952fz2x #RADStudio #Claude #AI #developers #DevTools

Using Local LLMs with Smart CodeInsight Learn how to get AI Assistance inside RAD Studio without sharing your code with cloud-hosted LLMs. code-partners.com/using-loca… #delphi #embarcadero #ollama #llm #ai

Was wondering about how codeinsight worked. :) Use of binja HLIL for this effort is cool. One of the hashes that was found by VT looks like a MacSync sample recently discussed. Using codeinights to query you can match on related samples some still undetected (although known).

𝑺𝒆𝒏𝒕𝒊𝒆𝒏𝒕: 𝑹𝒆𝒅𝒆𝒇𝒊𝒏𝒊𝒏𝒈 𝒕𝒉𝒆 𝑭𝒖𝒕𝒖𝒓𝒆 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑨𝑰 Artificial intelligence is undergoing a turning point. The world expects models that are not only powerful but also transparent, secure, and open to the community. @SentientAGI team has drawn attention with precisely this approach: at the NeurIPS conference, they presented a suite of innovations – OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs – to rethink how open AI should work. All these technologies share a common goal: to build an ecosystem of open AI in which models can be freely used and adapted while maintaining control, authenticity, and fair compensation for developers. They transform open code from a mere product into a living system of interaction, growth, and trust. 𝑶𝑴𝑳 1.0 – 𝑺𝒄𝒂𝒍𝒂𝒃𝒍𝒆 “𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏” 𝒇𝒐𝒓 𝑳𝑳𝑴𝒔 OML 1.0 is a cryptographic system that enables tracking the use of large language models without limiting their openness. Each model receives its own digital signature – a set of unique “fingerprints” ensuring authenticity and provenance control. Previously, only about 100 such pairs could be integrated; now – over 24,000, with no performance loss. This makes open models both secure and manageable on a global scale. 𝑪𝒐𝒅𝒆𝑰𝒏𝒔𝒊𝒈𝒉𝒕 𝑩𝒆𝒏𝒄𝒉𝒎𝒂𝒓𝒌 – 𝑴𝒆𝒂𝒔𝒖𝒓𝒊𝒏𝒈 𝑻𝒓𝒖𝒆 “𝑪𝒐𝒅𝒆 𝑰𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆” CodeInsight Benchmark is not just another code generation test. It’s a system that evaluates how well a model truly understands programming – how it reasons, integrates algorithms, and adapts to change. Instead of template tasks, it presents real-world scenarios: multi-module systems, variable conditions, and performance optimization. The results are impressive: compact models 10× smaller than typical LLMs reach top-tier efficiency using only ~20 % of training data. This opens doors for small teams and researchers – efficient AI without billion-dollar budgets. 𝑨𝒓𝒆𝒏𝒂𝑴𝒊𝒏𝒅 – 𝑺𝒆𝒍𝒇-𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑨𝒈𝒆𝒏𝒕𝒔 𝑻𝒉𝒓𝒐𝒖𝒈𝒉 𝑺𝒐𝒄𝒊𝒂𝒍 𝑮𝒂𝒎𝒆𝒔 ArenaMind is a platform where AI agents learn not from static datasets but through interaction. They play, observe, adapt, and develop emergent behavior – new strategies that arise naturally without human supervision. Hundreds of agents interact simultaneously, forming collective intelligence and distributed learning. Anyone in the community can add their own agent or scenario – influencing the evolution of the entire system. ArenaMind shows that the future of AI lies not in static models but in living, self-integrating systems that evolve alongside the community. 𝑺𝒆𝒄𝒖𝒓𝒆𝑳𝑳𝑴𝒔 – 𝑪𝒓𝒚𝒑𝒕𝒐𝒈𝒓𝒂𝒑𝒉𝒊𝒄 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑴𝒐𝒅𝒆𝒍𝒔 SecureLLMs solves the main dilemma of open-source AI: how to maintain openness without losing control. Every query and response undergoes cryptographic verification – the model remains accessible, yet its usage can be precisely tracked. Developers retain authorship, users gain trust, and the entire ecosystem gains security. Even after modifications, the model preserves its digital “fingerprint” – proof of authenticity and protection against tampering. SecureLLMs proves that transparency and security can coexist. It forms the foundation for fair monetization and control in open environments. 𝑺𝒆𝒏𝒕𝒊𝒆𝒏𝒕 𝒂𝒏𝒅 𝒕𝒉𝒆 𝑵𝒆𝒘 𝑨𝒈𝒆 𝒐𝒇 𝑶𝒑𝒆𝒏 𝑨𝑰 OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs together create a unified ecosystem where open models become secure, adaptive, and economically sustainable. @SentientAGI team demonstrates that open AI can be accessible, controllable, and efficient all at once. This marks the transition from static models to dynamic, self-learning, and transparent systems shaping the future of artificial intelligence. From now on, open AI is no longer a chaos of copies but an ecosystem of trust, collaboration, and rewarded contribution. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc @SentientAGI_UA @SentientAGI #SentientChat #GRID #AI #OpenAI #AIResearch #OpenSourceAI #NeurIPS

𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀: 𝗢𝗽𝗲𝗻𝗻𝗲𝘀𝘀 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗼𝘀𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 closes the last gap in open AI: how to ensure full access to code while guaranteeing security and monetization. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: ✓ Each model has cryptographic key-response pairs that act as digital signatures. ✓ Each request undergoes authenticity verification. ✓ All interactions are recorded, so usage can be traced and even economic sanctions applied in case of violations. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: ✓ Developers receive rewards for actual model usage. ✓ Users are confident in the authenticity of the code. ✓ Models retain authenticity even after fine-tuning. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 shows that 𝗼𝗽𝗲𝗻𝗻𝗲𝘀𝘀 ≠ 𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆. AI can be transparent and protected, community-based and monetized at the same time. 𝗦𝘂𝗺𝗺𝗮𝗿𝘆 Sentient is creating a new paradigm of open-source AI, where: - models have a unique digital trace (𝗢𝗠𝗟 1.0), - think analytically (𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸), - learn socially (𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱), - and are protected cryptographically (𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀). This is an ecosystem where open models become not just tools, but 𝗹𝗶𝘃𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 that grow, learn, and bring value to the entire community. Openness, security, monetization, and trust are now the 𝗳𝗼𝘂𝗿 𝗽𝗶𝗹𝗹𝗮𝗿𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗲𝗿𝗮 𝗼𝗳 𝗔𝗜. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗮𝗻𝗱 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱: 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗼𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗼𝗳 𝗔𝗜 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 Most AI tests check whether a model can “write code”. CodeInsight Benchmark determines whether it understands it. The test evaluates the model’s ability to: - think analytically, - integrate different modules, - adapt to changes in tasks. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝘀𝗵𝗼𝘄𝗲𝗱: models 10 𝘁𝗶𝗺𝗲𝘀 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝘁𝗵𝗮𝗻 𝗟𝗟𝗠𝘀 and trained on 20% 𝗼𝗳 𝗱𝗮𝘁𝗮 can perform the same tasks. This opens the way to resource-efficient open-source AI, accessible even to small teams. 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 A platform where AI agents learn through interaction, not just through data. Here arises emergent behavior - collective intelligence formed naturally. ✓Agents observe, collaborate, and adapt. ✓Systems become dynamic, not static. ✓ Knowledge spreads among hundreds of agents, creating a self-organizing ecosystem. 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 proves that 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 is the future of open-source AI. Collective behavior becomes not a side effect, but the main driving force of innovation. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 The world of artificial intelligence is changing faster than ever before. It is no longer enough to create a powerful model - it must be transparent, secure, and community-driven. At the NeurIPSconference, the company Sentient presented a series of technologies that can redefine the concept of open-source AI: 𝗢𝗠𝗟 1.0 - cryptographic fingerprinting of models 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 - measurement of “code intelligence” 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 - self-learning agents through interaction 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 - cryptographic control of open models These tools create a new ecosystem of open AI - where developers retain control and can monetize their contribution, while the community gains fair access to innovation. 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹 𝗼𝗳 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁: to make open AI scalable, secure, and sustainable. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 AI is experiencing a turning point. The world demands models that are not only powerful but also transparent, secure, and open to the community. At NeurIPS, 𝘁𝗵𝗲 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝘁𝗲𝗮𝗺 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗲𝗱 a set of groundbreaking technologies that redefine the approach to open-source models: 𝗢𝗠𝗟 1.0, 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸, 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀. Their common goal is to build an ecosystem of open AI where models can be freely used and adapted while maintaining control, authenticity, and monetization opportunities for developers. Thanks to these innovations, open-source AI becomes not just code but a living system of interaction, growth, and trust. 𝗢𝗠𝗟 1.0 – 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 “𝗙𝗶𝗻𝗴𝗲𝗿𝗽𝗿𝗶𝗻𝘁𝗶𝗻𝗴” 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝗢𝗠𝗟 1.0 is a cryptographic system that enables tracking the use of large language models without limiting their openness. Each model receives its own digital signature – a set of unique fingerprints that ensure authenticity and provenance control. Previously, it was possible to integrate only about 100 such pairs. Now – over 24,000, with no performance loss. This allows large-scale tracking and management of model usage across global projects, ensuring transparency and protecting developers. OML 1.0 transforms the idea of openness into a secure, controlled system where 𝘁𝗿𝘂𝘀𝘁 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗰𝗼𝗻𝘁𝗿𝗮𝗱𝗶𝗰𝘁 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿. 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 – 𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗧𝗿𝘂𝗲 “𝗖𝗼𝗱𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲” 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 is not just another code generation test. It’s a system that evaluates how well AI actually 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒔 𝒑𝒓𝒐𝒈𝒓𝒂𝒎𝒎𝒊𝒏𝒈 – how it thinks, integrates algorithms, and adapts to changes. Instead of template tasks, it presents real-world scenarios: multi-module systems, variable conditions, and performance optimization. The goal is to determine whether a model can reason, not merely repeat patterns. The results are impressive: 𝗰𝗼𝗺𝗽𝗮𝗰𝘁 𝗺𝗼𝗱𝗲𝗹𝘀, 10× 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝘁𝗵𝗮𝗻 𝘁𝘆𝗽𝗶𝗰𝗮𝗹 𝗟𝗟𝗠𝘀, 𝗿𝗲𝗮𝗰𝗵 𝘁𝗼𝗽-𝘁𝗶𝗲𝗿 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝘂𝘀𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 20% 𝗼𝗳 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮. This opens the door for small teams and researchers – 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗔𝗜 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀. 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 – 𝗦𝗲𝗹𝗳-𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗦𝗼𝗰𝗶𝗮𝗹 𝗚𝗮𝗺𝗲𝘀 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱 is a platform where AI agents learn not from static datasets but through interaction. They play, observe, adapt, and develop 𝒆𝒎𝒆𝒓𝒈𝒆𝒏𝒕 𝒃𝒆𝒉𝒂𝒗𝒊𝒐𝒓 – new strategies that arise naturally, without human supervision. Hundreds of agents interact simultaneously, forming collective intelligence and distributed learning. Anyone in the community can add their own agent or scenario – influencing the evolution of the entire system. ArenaMind demonstrates that the future of AI is not static models but 𝗹𝗶𝘃𝗶𝗻𝗴, 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗲𝘃𝗼𝗹𝘃𝗲 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 – 𝗖𝗿𝘆𝗽𝘁𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 addresses the main challenge of open-source AI: how to preserve openness without losing control. Every query and response undergoes cryptographic verification – the model remains accessible, yet its usage can be precisely tracked. Developers retain authorship, users gain trust, and the entire ecosystem gains security. Even after modifications, the model preserves its 𝒅𝒊𝒈𝒊𝒕𝒂𝒍 𝒇𝒊𝒏𝒈𝒆𝒓𝒑𝒓𝒊𝒏𝒕 – proof of authenticity and protection against tampering. 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 proves that transparency and security can coexist. It forms the foundation for fair monetization and control in open environments. 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗔𝗴𝗲 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 𝗢𝗠𝗟 1.0, 𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸, 𝗔𝗿𝗲𝗻𝗮𝗠𝗶𝗻𝗱, 𝗮𝗻𝗱 𝗦𝗲𝗰𝘂𝗿𝗲𝗟𝗟𝗠𝘀 together form a unified ecosystem where open models become secure, adaptive, and economically sustainable. 𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 shows that open AI can be accessible, controlled, and efficient all at once. This marks the transition from static models to 𝗱𝘆𝗻𝗮𝗺𝗶𝗰, 𝘀𝗲𝗹𝗳-𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 shaping the future of artificial intelligence. From now on, open-source AI is no longer a chaos of copies but an 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝘁𝗿𝘂𝘀𝘁, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝗲𝗱 𝗰𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

Sentient’s developments open a new era of open, transparent, and secure AI, combining high-performance models with tools for control, adaptation, and monetization for the community. The key technologies – OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs – form an integrated ecosystem where open models become not just tools, but an active environment for the development of community-driven intelligent systems. Taken together, these technologies shift the paradigm of open-source AI: from static models to dynamic, transparent, controllable, and self-learning systems, where security, scalability, efficiency, and collaboration form the foundation for sustainable development. Sentient demonstrates that open AI can be simultaneously accessible, reliable, and economically viable, setting a new standard for the entire industry. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗖𝗼𝗱𝗲𝗜𝗻𝘀𝗶𝗴𝗵𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸: 𝗗𝗲𝗲𝗽 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗔𝗜 “𝗖𝗼𝗱𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲” CodeInsight Benchmark is an advanced tool for assessing the real ability of AI models to program in practical conditions. Unlike traditional tests that check only syntax or the ability to complete template code, this benchmark analyzes deeper: how well the model understands programming logic, operates algorithms, integrates different modules, and adapts to dynamic scenarios. The goal of CodeInsight Benchmark is not just to determine whether a model can “write code,” but to evaluate true AI analytical thinking, its ability to solve complex problems, and optimize solutions for specific tasks. This allows distinguishing models that merely reproduce patterns from those that truly understand and can adapt code in real conditions. Features and Advantages: 1. 𝗥𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 CodeInsight Benchmark is not limited to basic tasks like sorting or searching. Instead, the model receives complex, multi-component assignments. ✅For example, it may integrate a data processing algorithm into a large system with numerous dependencies, testing module interaction, efficiency, and correctness. ✅2This approach assesses how well the model can adapt to changes in input data or scenarios, rather than just repeating familiar patterns. 2. 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 The benchmark encourages analytical thinking: ✅Models analyze new data, optimize code for specific conditions, and consider component interactions. ✅This reveals true “code understanding,” rather than superficial memorization of algorithms. ✅AI receives tasks where the solution of one module affects others, testing flexibility and adaptability. 3. 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 CodeInsight Benchmark enables evaluation of compact, resource-efficient models that use minimal computational resources but achieve top LLM performance: ✅For example, a model 10 times smaller than a standard LLM can successfully perform the same tasks using only 20% of training data. ✅This opens opportunities for researchers and small teams without access to large clusters or extensive datasets. 4. 𝗢𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 CodeInsight Benchmark establishes standards for evaluating “code intelligence” that can be applied in real production projects. Its impact on open-source AI is multifaceted: ✅Availability of compact yet powerful models lowers the entry barrier into AI development. ✅It stimulates innovation, allowing small teams or individual developers to create competitive solutions. ✅It sets practical criteria for comparing models’ ability to integrate code, optimize algorithms, and solve complex tasks. ✅It creates an environment for open experimentation and development of community models, where new approaches can be tested transparently and effectively. CodeInsight Benchmark changes the perception of AI evaluation in programming: it shows that not only model size or dataset volume determines capabilities, but flexibility, analytical thinking, and ability to integrate code into real systems. This opens new horizons for open-source AI development, making powerful and efficient models accessible to the wider community and establishing standards for real AI productivity in software development. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

𝗦𝗲𝗻𝘁𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗼𝗳 𝗢𝗽𝗲𝗻 𝗔𝗜 The world of artificial intelligence is changing rapidly. Today, it is critically important to create models that not only demonstrate high performance but also remain transparent, safe, and accessible to the community. Sentient, a leading player in AI research, presented at NeurIPS a whole series of groundbreaking technologies that radically transform the approach to open models: OML 1.0, CodeInsight Benchmark, ArenaMind, and SecureLLMs. The main goal of these developments is to create an open AI ecosystem where models can be safely used and adapted while retaining control over their origin and monetization opportunities for developers. Each of these tools plays a specific role in the development of open-source AI: from scalable model fingerprinting to evaluating true “code intelligence,” from building self-learning agents to cryptographic control over model usage. Thanks to these innovations, open AI becomes not just a set of codes and models, but a full-fledged ecosystem where the community can interact, create, improve, and safely apply intelligent systems. @sentient_chat @vivekkolli @0xsachi @LeaderX_btc #SentientChat #GRID #AI @SentientAGI_UA @SentientAGI

✨ 𝙌𝙪𝙖𝙡𝙞𝙩𝙮 𝙄𝙢𝙥𝙧𝙤𝙫𝙚𝙢𝙚𝙣𝙩𝙨 𝙞𝙣 𝙍𝘼𝘿 𝙎𝙩𝙪𝙙𝙞𝙤 𝟭𝟯 𝙁𝙡𝙤𝙧𝙚𝙣𝙘𝙚: 𝗗𝗲𝗹𝗽𝗵𝗶 𝗮𝗻𝗱 𝗖 𝗖𝗼𝗱𝗲 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 ✅ Classic Delphi CodeInsight returns—now alongside enhanced LSP performance. ✅ New Delphi Upgrade Advisor boosts compilation speed and tooling. ✅ Reworked Visual Assist adds 64-bit IDE support and improved stability. Explore what's new: 🔗 tinyurl.com/whats-new-RAD-13 #RADStudio #Delphi #CBuilder #developers #devtools

RAD Studio 12.3 : Smart CodeInsight Enhancements RAD Studio 12.3 updates Smart CodeInsight, its AI Assistant features in the IDE. Watch this quick overview to see more. code-partners.com/goto/?topi… #delphi #LLM #gemini #openai #claude #ollama

#Virustotal Uncovering a Colombian Malware Campaign with AI Code Analysis type:svg AND codeinsight:"Colombian" blog.virustotal.com/2025/09/…

Using Local LLMs with Smart CodeInsight Learn how to get AI Assistance inside RAD Studio without sharing your code with cloud-hosted LLMs. code-partners.com/using-loca… #delphi #embarcadero #ollama #llm #ai

【VirusTotal AI駆動型コード解析】VirusTotalがマルウェアアナリスト向けに、コードスニペットを受け取って機能説明を返す専用APIエンドポイントを発表し、リバースエンジニアリングの自動化を大幅に前進させた。 RSA 2023でのCode Insight発表から2年後にリリースされた新エンドポイント「api/v3/codeinsights/analyse-binary」は、Base64エンコードされたコードブロック（逆アセンブルまたは逆コンパイル）を受け取り、以下を返す： summary：関数の目的の高レベルビュー description：内部動作のステップバイステップ説明 最も革新的な機能は、アナリストが編集した応答履歴を含めることで、Code Insightが調査から「学習」し、より正確な分析を段階的に提供する点である。連続したリクエストをチェーンすることで、隠された動作をより効率的に発見できる。 IDA Pro用のVT-IDAプラグインも更新され、逆アセンブリインターフェースに新エンドポイントが直接統合された。受け入れられた分析は「CodeInsight Notebook」に保存され、後続のクエリのコンテキストとして使用される。実際の使用例では、アナリストが難読化された関数を選択してアンチデバッグルーチンの隠れたジャンプ技術を特定し、返却アドレス計算の詳細を追加編集してNotebookに保存することで、将来のクエリがこの強化されたコンテキストを活用できる。 テスト段階では、アナリストが初期コードトリアージに費やす時間を最大40%削減したと報告されている。プラグインは非英語文字列もハイライトして翻訳し、メモリオフセットを特定。これはマルウェアに埋め込まれたローカライゼーションやC2指令の理解に重要である。逆アセンブルビューと逆コンパイルビューの両方を活用し、それぞれの長所を組み合わせた簡潔な説明を提供する。 現在トライアルモードで利用可能で、コミュニティのフィードバックを募集中。AI生成の説明にはエッジケースの動作を見逃したり軽微なエラーが含まれる可能性があるため、アナリストのレビューが重要である。VirusTotalは今後、追加のファイル形式とリバースエンジニアリングプラットフォームへの拡張を計画しており、高度化する脅威の解明においてAIとの統合がマルウェア研究の不可欠なアシスタントとなることを目指している。 gbhackers.com/virustotal-lau…