Ogunya Busayo

💥DRC Seed System Digitalisation Roadmap💥 📡Partnership 4 Delivery & Scaling innovation Impact requires Partners' Understanding of each other's Vision, Mission & Needs is crucial 4 a more Impactful collaboration & development of the Roadmap 4 the good of our honorable farmers🛰

#SENASEM et #DANTIC (@minagri_rdc) et #INERA Digitalisent le Système Semencier pour une traçabilité et modernisation du secteur avec le logiciel @seedtracker, grâce à l'appui technique de @IITA_CGIAR et @FAORDCongo - Projets #PNDA (@BanqueMondiale) et #EmergeSys (@Groupe_AfDB).

🛰Digitalization is a Must for Sustsinable Seed System in DRC📡 💥First ONGOING 3 Days training workshop NOW SINCE Change we need 4 Sustainability & RELIABLE seed system 4 more Impact & the good of our honorable farmers💥 @l_lava @CGIARbreeding @CgiarFarming @kenloh

💥🛰Change We Need with Digitalization📡💥 🤝Successfully Launched, Successfully planned, The First training day on the Digitalization of the Seed System in DRC ended under the great team spirit & satisfaction of trainees demonstrating the importance of initiative & vision🤝

🛰Digitalization is a Must for Sustsinable Seed System in DRC📡 💥Change we need for Sustainability and reliability seed system for more Impact💥 L’envol du Secteur semencier ne se fera qu’avec la digitalisation graduelle et Intégrale de la filiere semenciere en RDC. @kenloh

🛰Digitalization is a Must for Sustsinable Seed System in DRC📡 💥Change we need for Sustainability and reliability seed system for more Impact💥 L’envol du Secteur semencier ne se fera qu’avec la digitalisation graduelle et Intégrale de la filiere semenciere en RDC. @IITA_CGIAR

💥Seed System Digitalization in DRC💥 📡SAVE THE MEMORABLE DATE🛰 L’envol du Secteur semencier ne se fera qu’avec la digitalisation graduelle et Intégrale de la filière semencière en RDC. Kudos to @IITA_CGIAR + @FAORDCongo + #PNDA Partnership 4 the good of Honorable Farmers💥

@MJAY_MOG @TeamCRonaldo The first wahala is ...Who's gonna be Captain?

@TeamCRonaldo If football gives us Ronaldo and Messi in the same team before retirement, that’s not just history that’s the final chapter of a legendary era. Would be a great duo to watch

🚨 𝗕𝗥𝗘𝗔𝗞𝗜𝗡𝗚: Inter Miami are closely monitoring Cristiano Ronaldo's situation at Al Nassr. David Beckham dreams of seeing Ronaldo and Messi play together on the same team before they retire.

@OkechukwuMbachu @sportbible You guys though...

@sportbible Now that he is not playing, his team is winning

Cristiano Ronaldo's next destination has become very clear and Saudi has already lined up his replacement... 😳

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@codewithimanshu Guide

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First large-scale study of AI agents actually running in production. The hype says agents are transforming everything. The data tells a different story. Researchers surveyed 306 practitioners and conducted 20 in-depth case studies across 26 domains. What they found challenges common assumptions about how production agents are built. The reality: production agents are deliberately simple and tightly constrained. 1) Patterns & Reliability - 68% execute at most 10 steps before requiring human intervention. - 47% complete fewer than 5 steps. - 70% rely on prompting off-the-shelf models without any fine-tuning. - 74% depend primarily on human evaluation. Teams intentionally trade autonomy for reliability. Why the constraints? Reliability remains the top unsolved challenge. Practitioners can't verify agent correctness at scale. Public benchmarks rarely apply to domain-specific production tasks. 75% of interviewed teams evaluate without formal benchmarks, relying on A/B testing and direct user feedback instead. 2) Model Selection The model selection pattern surprised researchers. 17 of 20 case studies use closed-source frontier models like Claude Sonnet 4, Claude Opus 4.1, and GPT o3. Open-source adoption is rare and driven by specific constraints: high-volume workloads where inference costs become prohibitive, or regulatory requirements preventing data sharing with external providers. For most teams, runtime costs are negligible compared to the human experts the agent augments. 3) Agent Frameworks Framework adoption shows a striking divergence. 61% of survey respondents use third-party frameworks like LangChain/LangGraph. But 85% of interviewed teams with production deployments build custom implementations from scratch. The reason: core agent loops are straightforward to implement with direct API calls. Teams prefer minimal, purpose-built scaffolds over dependency bloat and abstraction layers. 4) Agent Control Flow Production architectures favor predefined static workflows over open-ended autonomy. 80% of case studies use structured control flow. Agents operate within well-scoped action spaces rather than freely exploring environments. Only one case allowed unconstrained exploration, and that system runs exclusively in sandboxed environments with rigorous CI/CD verification. 5) Agent Adoption What drives agent adoption? It's simply the productivity gains. 73% deploy agents primarily to increase efficiency and reduce time on manual tasks. Organizations tolerate agents taking minutes to respond because that still outperforms human baselines by 10x or more. 66% allow response times of minutes or longer. 6) Agent Evaluation The evaluation challenge runs deeper than expected. Agent behavior breaks traditional software testing. Three case study teams report attempting but struggling to integrate agents into existing CI/CD pipelines. The challenge: nondeterminism and the difficulty of judging outputs programmatically. Creating benchmarks from scratch took one team six months to reach roughly 100 examples. 7) Human-in-the-loop Human-in-the-loop evaluation dominates at 74%. LLM-as-a-judge follows at 52%, but every interviewed team using LLM judges also employs human verification. The pattern: LLM judges assess confidence on every response, automatically accepting high-confidence outputs while routing uncertain cases to human experts. Teams also sample 5% of production runs even when the judge expresses high confidence. In summary, production agents succeed through deliberate simplicity, not sophisticated autonomy. Teams constrain agent behavior, rely on human oversight, and prioritize controllability over capability. The gap between research prototypes and production deployments reveals where the field actually stands. Paper: arxiv.org/abs/2512.04123 Learn design patterns and how to build real-world AI agents in our academy: dair-ai.thinkific.com

🛰📡EmergenSys-Project in DRC📱🖥 🤝Partnership 4 Delivery & Scaling innovation Impact like @Seesdtracker requires coherent Strategic planification for Partners' Understanding of each other's Vision, Mission & Needs. Long live @IITA_CGIAR + @FAODRCongo Partnership!💥 @Taat_Africa

@OgunyaFort Lol...You happen to the only young developer i know who's saying upcoming developers should rely less on AI.... Couple of them I know cannot craft put solutions anymore via critical thinking...AI now does that for them drastically reducing their creativity.

As a developer who is still learning, try to depend less on AI, and you'll notice some changes. You're welcome.

I worked on this project a few weeks ago, and it was an awesome experience for me. I was able to learn how to implement some small, noticeable features and understand how they work. Btw, this is a surprise in the settings page. sub-ai.framer.website

@Auto_poacher Most of the people commenting haven't used or driven a Crosstour before....They really don't know.Crosstour is even cheaper to maintain than Venza. Parts are cheaper as well....imagine changing Crosstour Gear for 500k....and for Venza at 2.1m Crosstour is my pick anyway anytime

Experts in the house please I need some advice based on personal experience about the overall reliability of VENZA vs Crosstour.