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An update on the Fungo Genesis auction: We are issuing a total of $84,150 in refunds. Today, we did not reach the level of participation we were aiming for, and rather than reduce the supply, we have decided not to complete the auction. This is not an easy message to write. For months, we have put everything into building the infrastructure needed to make encrypted NFTs possible on Ethereum. But the level of participation we saw today was not enough for us to move forward with the sale as planned. Throughout development, the Zama team provided technical support and helped us build the best system we could deliver today. This does not mean Zama stands behind or endorses our project. FungoLabs is an independent project building in the Zama ecosystem, and all decisions are our own. The numbers we shared throughout this journey were real. More than 3,000 projects applied to our launchpad, over 12,000 people joined our X campaign, and we received more than 30,000 whitelist applications. We will share a separate breakdown of these numbers soon. We also want to be transparent about one thing: projects like this cannot run on excitement alone. Infrastructure, servers, onchain activity, and the work of the team all come with real costs. Without enough support, carrying that load is simply not sustainable. Any payments made by whitelist minters, along with the full amount of every bid, will be refunded. Refunds will be sent directly from the project treasury to your wallets. You do not need to take any action. Thank you to everyone who gave us their time, believed in what we were building, and supported us throughout this journey. We are sorry we could not meet your expectations. This is not the end. We will keep building on Zama.

Video content creation sounds simple, but what if you don’t have time to: • Write the script, • Prepare the visuals, • Generate the voiceover, • Create the subtitles, • And finally render the video? This is why we built Noustiny on top of @NousResearch Hermes Agent by adding 12 generic Hermes tools + 13 generic Hermes skills, bringing the whole process into one single flow. How does it work? Let’s take a closer look 👇 ———— 1- Story state: context, tree, motifs: Hermes had no built-in narrative-state primitive for tracking canon, branching story structure, and recurring motifs. So we added three generic Hermes tools for this: → story_tree_graph: Manages the story tree structure. It handles operations like canon path, descendants, and splice insertion points. → narrative_context_builder: Walks the canon chain and returns the live context every narrative skill should reason against. This includes recent chain, mood, and character state. → motif_tracker: Remembers recurring motifs across the story arc. For example, a sword introduced in beat 2 can reappear meaningfully in later scenes. ———— 2- Character / cast pipeline: Hermes had no built-in primitive for cast extraction or character continuity. So we added a four-tool character pipeline: → story_copyright_detector: Handles IP scrubbing. For example, “Iron Man” is converted into an IP-free character description before the image API ever sees it. → character_sheet_builder: Produces 1 to 4 characters. For each character, it creates an IP-free visual description and a hero-portrait prompt. These portraits become the reference frames used across later storyboard scenes. → character_registry_lookup: Finds a character by name inside the cast sheet and attaches the correct portrait reference to each beat. → character_alias_resolver: Resolves aliases like “Mr. Stark” into the main character name. This way, the same character keeps one portrait reference even if they appear under different names. ———— 3- Voice pipeline: Hermes had no built-in primitive for audio acquisition or voice cloning. So we added the full voice chain, and the agent dispatches it autonomously in order: → narration_voice_director: The director-agent reads the seed + story and returns persona_label, search_query, and fallback_query. → voice_sample_builder: Uses yt-dlp + ffmpeg. It accepts a URL, an 11-character ID, or a free-text query. It runs ytsearch5 with dead-video tolerance and normalizes the audio to 24 kHz mono PCM. → voice_clone_synthesize: Wraps ElevenLabs IVC + timestamps. The voice ID is cached by reference SHA. Per-character alignment comes through the same audio call at no extra cost. → voice_clone_cleanup: Frees the cached voice ID after render so orphan voices do not accumulate. ———— 4- Render: Hermes had no built-in video-render entry. So we added the final render tool: → noustiny_storybook: The agent dispatches it as the final step of the chain. One tool call drives the FastAPI render service end to end and emits the mp4. ———— 5- Skills: 13 generic Hermes skills added into skills/creative/: The branching engine in Noustiny works like a council of narrative skills. Each skill is loaded by the gateway as a system prompt and orchestrated in this order: → narrative-brainstorm: Proposes 2 to 3 next-checkpoint options from the canon chain. → narrative-writer-assist: Writes a spliced insert beat that fits the parent and child. → narrative-continuity-critic: Audits downstream beats against the new insert. → narrative-rewriter: Updates the stale beats flagged by the continuity critic. → narrative-judge: Approves or rejects the rewrite against the original flow. → narrative-scene-qa: Checks each beat for consistency, length, and register. → narrative-writer: Finalizes the chosen branch as polished prose. After one splice, this cascade walks downstream by itself until the canon becomes coherent again. ———— 6- Visual + IP pipeline: On the visual side, the goal is not just generating scenes. It is also preserving character continuity and IP safety. This pipeline runs through these skills: → visual-prompt-builder: Turns a beat into an IP-free image prompt and reads the character-sheet references. → scene-composition: Defines shot framing, scene composition, and layout rules. → story-copyright-detector: Skill counterpart of the same-named tool. It can be used for direct slash-command invocation. → character-sheet-builder: Skill counterpart of the same-named tool. Defines cast extraction rules and the IP-free portrait-prompt format used to seed character consistency across the storyboard. → storybook-intro: Generates the cinematic intro page for the render. ———— 7- Voice skill: → narration-voice-director: Defines persona reasoning rules and supports the decision logic behind the same-named voice tool. ———— 8- Pattern: Hermes baseline already had the gateway, agent loop, skill registry, and tool registry. We extended that foundation with 12 generic Hermes tools + 13 generic Hermes skills and organized the system into four main pipelines: • story-state • character continuity • voice • render The important part is this: Noustiny is not a hardcoded system locked inside a single app. A Telegram bot, Discord bot, CLI session, or third-party Next.js app can call the same gateway and use the same tool + skill chains. - No app glue. - No hardcoded prompts. - A drop-in, registry-compatible, agent-native video creation flow. ✅Github: github.com/UfukNode/Noustiny

The Hermes Agent Hackathon Starts Now Show us what Hermes Agent can do: build something unique, creative, and useful. 1st: $7,500 2nd: $2,000 3rd: $500 To enter, make a tweet tagging @NousResearch with a video demo and a brief writeup, then send the tweet link to the submissions channel in our Discord. Entries will be judged by Nous staff on creativity, usefulness, and presentation. Submissions are due EOD Sunday 03/16.

Zama Developer Programı kapsamında geliştirdiğim app top uygulama seçildi == 🇹🇷🏆 Neler bizi öne çıkardı ? ▪️ FHE'nin tüm nimetlerinin App'de aktif olarak kullanılması ▪️ Pre-production ürün + live app ▪️ Tasarım ▪️ Github doc. Destek ♥️ #ZamaCreatorProgram #ZamaFHE @zama

Zolymarket. A fully decentralized prediction market leveraging FHE to keep bet amounts private while maintaining transparent market outcomes.