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Jules #12 (Etheria) [Commission] Audio By: Evilaudio Voice By: Delalicious VA 🔞🎙️(RETIRED) Male By: IceDev Etheria By: x_RedEyes 🔞 | (OPEN COMMISSIONS) full animation (4:30)+No watermark on 🅿️atre0n #Fortnite #fortniteporn #etheria #R34 #Rule34 #animation3d #3dporn

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Sessho3D (Commissions open)

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NSFW#Fortnite#fortniteporn#etheria#R34#Rule34#animation3d#3dporn
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fallow me on patreon and suscribestar or newgrounds too, i'm posting there some thing too cast: #17 by: Leo Greystone🔞🎙️ #18 by: 💖 SilkyMilk - Adult VA 💖 Cell by: RB After Dark (NSFW COMMISSION OPEN) Music by: Mono Memory

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MonsterChow

67,555 views • 3 years ago

VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.
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VoxCPM 2 just dropped by OpenBMB Only 2B-param open-source TTS (Text-to-Speech) model built for production-grade multilingual voice work. Apache-2.0 license, Can run on only 8GB VRAM. • Eliminates the "robotic" feel of traditional TTS, delivering prosody and emotional depth suitable for high-stakes professional environments like filmmaking, gaming, animation, and audiobooks. • 30-language multilingual: no language tag needed, just type in a supported language and generate directly. • Voice design: create a brand-new voice from a text description alone, like age, tone, pace, or emotion. No reference audio required. Describe the desired voice characteristics (gender, age, tone, emotion, pace …) in Control Instruction, and VoxCPM2 will craft a unique voice from your description alone. • Controllable cloning: clone from a short clip, then steer delivery style without losing the speaker’s core voice. • Ultimate cloning: use reference audio + transcript for continuation-style cloning that keeps the tiny vocal details. • 48kHz output: takes 16kHz reference audio and produces studio-quality speech without an external upsampler. • Real-time ready: around 0.3 RTF on RTX 4090, even lower with Nano-VLLM. • Commercial use: Apache-2.0 licensed. Developer-Friendly Infrastructure: - Native Torch Inference: Direct support for PyTorch-based workflows. - Training Flexibility: Supports both full-parameter and LoRA fine-tuning for specific domain adaptation. - Production Readiness: Compatible with voxcpm-nanovllm for large-scale, high-concurrency deployment.

Rohan Paul

13,541 views • 5 months ago

These are 5 AI investment themes we're betting on for the next 12-18 months. We're focused on the foundational challenges that will unlock the next wave of AI applications. 🧠 Theme #1: Persistent Memory The biggest unsolved problem in AI. Current solutions (vector DBs, RAG, longer context windows) aren't cutting it. Two types needed: • Long-term memory (organizational context) • Identity persistence (consistent AI personality) No scaling laws exist for this yet. Whoever cracks it first doesn't just win a feature—they unlock entirely new AI categories. 🌐 Theme #2: Seamless Communication MCP is just the starting gun, not the finish line. When AIs can communicate seamlessly, they'll eliminate every platform moat built on convenience. Your AI won't care about Amazon's one-click or Apple's ecosystem lock-in. Platform disruption at unprecedented scale. 🎙️ Theme #3: AI Voice Video is the future, but voice is ready TODAY. • Fidelity breakthrough ✓ • Real-time latency solved ✓ • Existing infrastructure everywhere ✓ Enterprise opportunity is massive: logistics coordination, trading desks, supply chain calls. Millions of voice interactions are waiting for AI automation. 🛡️ Theme #4: Secure Trust In the AI future, you'll need hundreds of security agents per human. Unlike physical security (limited by space/cost), digital security scales infinitely. Every AI agent creates new attack vectors. Every interaction needs protection. The math is simple: More AI agents = exponentially more security agents needed. 🔓 Theme #5: Open Source Open source is at a critical crossroads. The next 12 months determine if innovation stays democratized or gets locked behind corporate walls. We're committed to ensuring the AI future is built by everyone, not just the extremely well-funded giants. The meta-theme: These aren't just product features. They're the foundational infrastructure that will enable the next generation of AI applications.
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Konstantine Buhler

106,372 views • 1 year ago

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 Nous Research 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:
6:43

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 Nous Research 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:

Ufuk

28,972 views • 5 months ago

They knew all along about false flag Maidan massacre: Deputy head of Zelensky Office of President of Ukraine told Western ambassadors on February 19, 2014 that Maidan protesters were shot in the back by other Maidan activists & police did not shoot them. Nobody is convicted for killings of 27 Maidan protesters & 12 policemen and Internal Troops servicemen on Maidan on February 18-19, 2014. Tatarov, who then was deputy head of investigative department of Ministry of Internal Affairs, was first to publicly refer to false flag Maidan massacre one day before 49 Maidan activists & 4 police officers were killed in false flag mass killing. Oleh Tatarov, who then was the deputy head of the investigative department of the Ministry of Internal Affairs, was the first to publicly refer to the false flag Maidan massacre one day before 49 Maidan activists and 4 police officers were killed in the false flag mass killing. My open access book "The Maidan Massacre in Ukraine: The Mass Killing That Changed the World" and four peer-reviewed journal articles show overwhelming evidence that the Maidan massacre was a false flag mass killing of Maidan activists and police with involvement of far-right and oligarchic elements of the Maidan opposition in order to overthrow the Ukrainian government. A member of the Maidan leadership from the Fatherland party was filmed on February 18, 2014, evacuating a car with a hunting rifle, equipped with a silencer and optics, of a Maidan activist, who was stopped by other Maidan activists. A person who helped in the evacuation became an aide to one of leading members of Fatherland, who became the Minister of Internal Affairs after Euromaidan, and whose ministry was involved in investigations of killings of the protestors and the police on the Maidan. Three protesters were killed and up to 33 wounded with hunting pellets in the same area around the same time. The Ukrainian investigation did not identify any suspects in killings of 12 protesters during a storming of the Maidan by the police in the evening of February 18 and around midnight. The GPU, the special parliamentary commission, a media report based on the GPU investigation, and other evidence reported that about half of them were killed by pellets or hunting bullets. Maidan activists armed with hunting firearms, in particular, from the far-right Right Sector and Svoboda-linked Maidan company, were filmed by various media and social media. They also were reported by numerous eyewitnesses, primarily, Maidan protesters. There has been no investigations of involvements of groups of Maidan snipers in Hotel Ukraina and the Trade Union building and other nearby buildings for killings of these protesters in spite of various such evidence and in spite of similar killings of the police around the same time and place, in particular, by hunting pellets and bullets. For instance, a Hotel Ukraina employee said that he witnessed a group of snipers in Maidan style uniforms and with weapons in cases entering the hotel shortly before the massacre started on February 18. A Fatherland deputy said that he witnessed protesters killed near him on the Maidan by shooters from the Hotel Ukraina and Kozatsky Hotel on the same day. Kyiv court rulings specifically refer to two Right Sector activists, who were wounded during a Right Sector attack of a separatist checkpoint in Sloviansk on April 20, 2014 and many other Right Sector members as suspects in GPU investigation in killings and wounding the police on the Maidan. The court decisions stated that the weapons used by the wounded checkpoint attackers were the same weapons from which two Internal Troops servicemen were killed and three other policemen wounded on the Maidan on February 18, 2014. There is also evidence that there were armed Maidan shooters linked to the oligarchic Fatherland Party. A top person in the security of the Fatherland party admitted in the Ukrainian media shooting the police on the Maidan. The Ukrainian media reported five years afterwards that Kyiv prosecutors found that killers of two traffic policemen in Kyiv on February 19 were among Maidan snipers. These killers received phone calls from prominent female and male parliament deputies from one of the oligarchic Maidan parties after killing these policemen during a traffic stop. The investigation after these findings was transferred to the police and completely stalled. The two killed policemen were included in the “Heavenly Hundred” of killed Maidan protesters, and their killing was publicly attributed to pro-Yanukovych government titushki.
2:07

They knew all along about false flag Maidan massacre: Deputy head of Zelensky Office of President of Ukraine told Western ambassadors on February 19, 2014 that Maidan protesters were shot in the back by other Maidan activists & police did not shoot them. Nobody is convicted for killings of 27 Maidan protesters & 12 policemen and Internal Troops servicemen on Maidan on February 18-19, 2014. Tatarov, who then was deputy head of investigative department of Ministry of Internal Affairs, was first to publicly refer to false flag Maidan massacre one day before 49 Maidan activists & 4 police officers were killed in false flag mass killing. Oleh Tatarov, who then was the deputy head of the investigative department of the Ministry of Internal Affairs, was the first to publicly refer to the false flag Maidan massacre one day before 49 Maidan activists and 4 police officers were killed in the false flag mass killing. My open access book "The Maidan Massacre in Ukraine: The Mass Killing That Changed the World" and four peer-reviewed journal articles show overwhelming evidence that the Maidan massacre was a false flag mass killing of Maidan activists and police with involvement of far-right and oligarchic elements of the Maidan opposition in order to overthrow the Ukrainian government. A member of the Maidan leadership from the Fatherland party was filmed on February 18, 2014, evacuating a car with a hunting rifle, equipped with a silencer and optics, of a Maidan activist, who was stopped by other Maidan activists. A person who helped in the evacuation became an aide to one of leading members of Fatherland, who became the Minister of Internal Affairs after Euromaidan, and whose ministry was involved in investigations of killings of the protestors and the police on the Maidan. Three protesters were killed and up to 33 wounded with hunting pellets in the same area around the same time. The Ukrainian investigation did not identify any suspects in killings of 12 protesters during a storming of the Maidan by the police in the evening of February 18 and around midnight. The GPU, the special parliamentary commission, a media report based on the GPU investigation, and other evidence reported that about half of them were killed by pellets or hunting bullets. Maidan activists armed with hunting firearms, in particular, from the far-right Right Sector and Svoboda-linked Maidan company, were filmed by various media and social media. They also were reported by numerous eyewitnesses, primarily, Maidan protesters. There has been no investigations of involvements of groups of Maidan snipers in Hotel Ukraina and the Trade Union building and other nearby buildings for killings of these protesters in spite of various such evidence and in spite of similar killings of the police around the same time and place, in particular, by hunting pellets and bullets. For instance, a Hotel Ukraina employee said that he witnessed a group of snipers in Maidan style uniforms and with weapons in cases entering the hotel shortly before the massacre started on February 18. A Fatherland deputy said that he witnessed protesters killed near him on the Maidan by shooters from the Hotel Ukraina and Kozatsky Hotel on the same day. Kyiv court rulings specifically refer to two Right Sector activists, who were wounded during a Right Sector attack of a separatist checkpoint in Sloviansk on April 20, 2014 and many other Right Sector members as suspects in GPU investigation in killings and wounding the police on the Maidan. The court decisions stated that the weapons used by the wounded checkpoint attackers were the same weapons from which two Internal Troops servicemen were killed and three other policemen wounded on the Maidan on February 18, 2014. There is also evidence that there were armed Maidan shooters linked to the oligarchic Fatherland Party. A top person in the security of the Fatherland party admitted in the Ukrainian media shooting the police on the Maidan. The Ukrainian media reported five years afterwards that Kyiv prosecutors found that killers of two traffic policemen in Kyiv on February 19 were among Maidan snipers. These killers received phone calls from prominent female and male parliament deputies from one of the oligarchic Maidan parties after killing these policemen during a traffic stop. The investigation after these findings was transferred to the police and completely stalled. The two killed policemen were included in the “Heavenly Hundred” of killed Maidan protesters, and their killing was publicly attributed to pro-Yanukovych government titushki.

Ivan Katchanovski

68,056 views • 8 months ago

Voice AI can pass a Turing test. For about a minute. That's a generated clip, though. Have a human actually talk back and the number collapses to six or seven seconds, roughly where generated voice sat three years ago. One reason: real conversation isn't turn-based. Around 20% of the time more than one person is speaking, and laughter drives a lot of that overlap, since you laugh at a joke while it's still being told. We also adjust our pacing toward whoever we're talking to without noticing we're doing it. Voice models struggle with all of this. Full-duplex voice, where a model listens and speaks at the same time, is still extremely early. So an agent can know your joke is funny and still have to wait until you've finished before it laughs, by which point the timing has killed it. Miso Labs CEO & Co-Founder, Aoden Teo, describes a second consequence: agents get pushed toward almost "psychotically emotive" behavior. If they can only talk once you've stopped, they need some other way to show they were listening. You finish your sentence, and the thing goes "Hmm?". You've heard it. Underneath that sits an architecture problem. Voice models have to respond fast, which constrains how large they can be, and fast means something different here than it does in text. Working with an LLM like Claude, you care how quickly it finishes your code, more than how quickly it starts. Voice inverts that. Nobody needs 10 hours of audio generated in two seconds, because nobody can listen to 10 hours of audio in two seconds; what matters is reaction time. Most architectural decisions trade latency against throughput, and Aoden expects voice to keep moving away from LLM-style designs toward ones built around very low latency. Miso Labs is already pushing on it. Miso-1 got 3,000+ stars on GitHub and 5 million views on X, and they record data in their own LA studio because the internet doesn't contain every kind of audio a voice model might need. Nobody has released a podcast of someone reading millions and millions of email addresses, and people still want voice models that can read email addresses aloud, so teams end up generating some very strange training data themselves. The clip isn't the hard part. The hard part starts when you talk back. "The most emotive foundation models for voice" 🎙️Aoden Teo, CEO & Co-Founder, Miso Labs on Fondo.com START 1:03 Miso-1: 3K+ GitHub stars + 5M X views 1:59 Why emotiveness matters for games, UGC + interactive products 3:06 Measuring progress in voice AI with longer Turing tests 4:01 Why interactive conversation is harder than generating convincing clips 5:08 Full-duplex voice, interruptions + why laughter matters 6:04 Latency vs. throughput - and why voice differs from LLMs 7:09 Miso's LA recording studio + the challenge of voice training data 9:02 Talking teddy bears, UGC, anime + unexpected voice AI use cases 10:19 From serious chess player to math obsession to building Miso Labs 12:11 The surprise YC interview
14:15

Voice AI can pass a Turing test. For about a minute. That's a generated clip, though. Have a human actually talk back and the number collapses to six or seven seconds, roughly where generated voice sat three years ago. One reason: real conversation isn't turn-based. Around 20% of the time more than one person is speaking, and laughter drives a lot of that overlap, since you laugh at a joke while it's still being told. We also adjust our pacing toward whoever we're talking to without noticing we're doing it. Voice models struggle with all of this. Full-duplex voice, where a model listens and speaks at the same time, is still extremely early. So an agent can know your joke is funny and still have to wait until you've finished before it laughs, by which point the timing has killed it. Miso Labs CEO & Co-Founder, Aoden Teo, describes a second consequence: agents get pushed toward almost "psychotically emotive" behavior. If they can only talk once you've stopped, they need some other way to show they were listening. You finish your sentence, and the thing goes "Hmm?". You've heard it. Underneath that sits an architecture problem. Voice models have to respond fast, which constrains how large they can be, and fast means something different here than it does in text. Working with an LLM like Claude, you care how quickly it finishes your code, more than how quickly it starts. Voice inverts that. Nobody needs 10 hours of audio generated in two seconds, because nobody can listen to 10 hours of audio in two seconds; what matters is reaction time. Most architectural decisions trade latency against throughput, and Aoden expects voice to keep moving away from LLM-style designs toward ones built around very low latency. Miso Labs is already pushing on it. Miso-1 got 3,000+ stars on GitHub and 5 million views on X, and they record data in their own LA studio because the internet doesn't contain every kind of audio a voice model might need. Nobody has released a podcast of someone reading millions and millions of email addresses, and people still want voice models that can read email addresses aloud, so teams end up generating some very strange training data themselves. The clip isn't the hard part. The hard part starts when you talk back. "The most emotive foundation models for voice" 🎙️Aoden Teo, CEO & Co-Founder, Miso Labs on Fondo.com START 1:03 Miso-1: 3K+ GitHub stars + 5M X views 1:59 Why emotiveness matters for games, UGC + interactive products 3:06 Measuring progress in voice AI with longer Turing tests 4:01 Why interactive conversation is harder than generating convincing clips 5:08 Full-duplex voice, interruptions + why laughter matters 6:04 Latency vs. throughput - and why voice differs from LLMs 7:09 Miso's LA recording studio + the challenge of voice training data 9:02 Talking teddy bears, UGC, anime + unexpected voice AI use cases 10:19 From serious chess player to math obsession to building Miso Labs 12:11 The surprise YC interview

David J Phillips

36,612 views • 1 month ago

this OpenClaw🦞 agent replaced my $8K/month content strategist for $30 😱 i'll show you EXACTLY how to build it live with The Boring Marketer vibe marketers. here's the system: step 1: scan 1000s+ creators for outlier signals → virlo watches your niche 24/7 → it finds outlier videos that beat the creator's OWN average by 10x-50x. → last week it flagged a 51x outlier. 4,800 avg views. one video: 249,000. step 2: pull the raw evidence → Adrian | The Web Scraping Guy 's ScrapeCreators fetches the full package → thumbnail, caption, stats, creator context → this gives the AI everything it needs to analyze. step 3: break down WHY it worked (7 dimensions) → Content DNA runs each video through Gemini 3 Flash via @OpenRouterAI → topic, angle, hook structure (visual + text + spoken), story beats, visual format, key visuals, audio → the output is a "brick." the portable structure that made people stop scrolling. step 4: rank the reusable bricks → OpenClaw🦞 compares all 10 breakdowns → scores each brick by portability and frequency → bricks showing up across 3+ creators = highest confidence bets step 5: generate 10 concepts in your voice + 3 psychology frameworks → learns your voice so output sounds like you → then runs every concept through Puppet Strings (desire), Scroll Traps (attention), and Care to Click (action) → you hit emotion and desire. not just features. every hook is copy-paste ready. step 6: delivers 10 concepts every monday at 8am → cron runs the whole pipeline overnight → pick 3. film. post. done. input: your niche output: 10 proven content concepts every monday a GOOD content strategist costs $8-10K/month. this runs for ~$30/month in API costs. on march 12, me and The Boring Marketer vibe marketing community are building this system live: - brand voice file - content radar scanning your niche - content DNA breaking it down - weekly cron, scheduled and firing you leave with a running system (not a repo to figure out later) comment VIEWS + like + follow (must follow so i can DM you the link to join)
0:31

this OpenClaw🦞 agent replaced my $8K/month content strategist for $30 😱 i'll show you EXACTLY how to build it live with The Boring Marketer vibe marketers. here's the system: step 1: scan 1000s+ creators for outlier signals → virlo watches your niche 24/7 → it finds outlier videos that beat the creator's OWN average by 10x-50x. → last week it flagged a 51x outlier. 4,800 avg views. one video: 249,000. step 2: pull the raw evidence → Adrian | The Web Scraping Guy 's ScrapeCreators fetches the full package → thumbnail, caption, stats, creator context → this gives the AI everything it needs to analyze. step 3: break down WHY it worked (7 dimensions) → Content DNA runs each video through Gemini 3 Flash via @OpenRouterAI → topic, angle, hook structure (visual + text + spoken), story beats, visual format, key visuals, audio → the output is a "brick." the portable structure that made people stop scrolling. step 4: rank the reusable bricks → OpenClaw🦞 compares all 10 breakdowns → scores each brick by portability and frequency → bricks showing up across 3+ creators = highest confidence bets step 5: generate 10 concepts in your voice + 3 psychology frameworks → learns your voice so output sounds like you → then runs every concept through Puppet Strings (desire), Scroll Traps (attention), and Care to Click (action) → you hit emotion and desire. not just features. every hook is copy-paste ready. step 6: delivers 10 concepts every monday at 8am → cron runs the whole pipeline overnight → pick 3. film. post. done. input: your niche output: 10 proven content concepts every monday a GOOD content strategist costs $8-10K/month. this runs for ~$30/month in API costs. on march 12, me and The Boring Marketer vibe marketing community are building this system live: - brand voice file - content radar scanning your niche - content DNA breaking it down - weekly cron, scheduled and firing you leave with a running system (not a repo to figure out later) comment VIEWS + like + follow (must follow so i can DM you the link to join)

Matthew Berman

149,893 views • 7 months ago

Stupendous achievements of the DMK government that should strike voters at the right time! Since the opposition parties are busy fighting with each other, I thought the onus is on an ordinary voter like me to list the achievements of the DMK government that I believe will create some kind of awareness. Below are some of the achievements of the DMK government over the past 5 years! 1. Blatant nepotism through elevation of Udhayanidhi 2. Abysmal financial management - State debt from 5+lakh crores in 2021 to 9+ lakh crores in 2026 3. Large scale corruption across all departments 4. TASMAC Scam 5. Cyanide in Government liquor 6. Ganjaa 7. Synthetic drugs 8. Monopoly of drinking water - Springs 9. Increasing crimes because of intoxication 10. Vengaivayal 11. Nanguneri 12. Marakkanam illicit liquor deaths (around 14) 13. Kallakurichi illicit liquor deaths (around 65) 14. Murder of the state president of a national party - Armstrong 15. Honour killings of Dalits - Kavin 16. Deteriorating law & order situation 17. Disaster mismanagement – Chennai floods 2023 18. Women safety becoming a laughing stock – lady cop molested in a DMK conference 19. Anna University scandal 20. EB charges with monthly meter reading not fulfilled 21. No gas subsidiary 22. Inflated property registration fee for no reason 23. Samsung protests 24. Metro DPR drama 25. Irrational & unwarranted dual with Centre affecting State's progress. Development & infra works are slowed 26. Protests from all corners – Nurses/Techers etc 27. Increasing Lock up deaths 28. Failed promise on reducing fuel and gas prices 29. Illogical car race that never made any difference to the common man 30. Killing Amma canteens 31. Air show deaths (5 civilians due to dehydration) 32. Sidelining honest and performing ministers – PTR 33. Sanatana Dharma eradication 34. Jaffar saddiq drug scandal 35. Attempt to open liquor shops in marriage halls and sports stadiums 36. NEET abolition drama 37. Atrocious illegal sand mining, supposedly to the tune of 60,000 crore declared by a drone study by IIT 38. Abuse of temple funds by HR&CE headed by Sekhar Babu and directing those funds to build colleges and marriage halls. 39. Crops worth several crores getting wasted 40. Abolition of TNPSC 41. Failure to appoint a full time DGP AKA Head of Police Force 42. Choking freedom of speech by witch-hunting critics – Savukku Shankar and, 43. Taking control of the film industry for "ALL" purposes possible! The current government is by far the worst Tamil Nadu has ever seen. Voters should bear this in mind when they exercise their right in May 2026. Udhayanidhi could become the CM if DMK wins again. Your future is in your hands. BTW – If I have missing any of DMK’s achievements, please let me know through your comments!
4:01

Stupendous achievements of the DMK government that should strike voters at the right time! Since the opposition parties are busy fighting with each other, I thought the onus is on an ordinary voter like me to list the achievements of the DMK government that I believe will create some kind of awareness. Below are some of the achievements of the DMK government over the past 5 years! 1. Blatant nepotism through elevation of Udhayanidhi 2. Abysmal financial management - State debt from 5+lakh crores in 2021 to 9+ lakh crores in 2026 3. Large scale corruption across all departments 4. TASMAC Scam 5. Cyanide in Government liquor 6. Ganjaa 7. Synthetic drugs 8. Monopoly of drinking water - Springs 9. Increasing crimes because of intoxication 10. Vengaivayal 11. Nanguneri 12. Marakkanam illicit liquor deaths (around 14) 13. Kallakurichi illicit liquor deaths (around 65) 14. Murder of the state president of a national party - Armstrong 15. Honour killings of Dalits - Kavin 16. Deteriorating law & order situation 17. Disaster mismanagement – Chennai floods 2023 18. Women safety becoming a laughing stock – lady cop molested in a DMK conference 19. Anna University scandal 20. EB charges with monthly meter reading not fulfilled 21. No gas subsidiary 22. Inflated property registration fee for no reason 23. Samsung protests 24. Metro DPR drama 25. Irrational & unwarranted dual with Centre affecting State's progress. Development & infra works are slowed 26. Protests from all corners – Nurses/Techers etc 27. Increasing Lock up deaths 28. Failed promise on reducing fuel and gas prices 29. Illogical car race that never made any difference to the common man 30. Killing Amma canteens 31. Air show deaths (5 civilians due to dehydration) 32. Sidelining honest and performing ministers – PTR 33. Sanatana Dharma eradication 34. Jaffar saddiq drug scandal 35. Attempt to open liquor shops in marriage halls and sports stadiums 36. NEET abolition drama 37. Atrocious illegal sand mining, supposedly to the tune of 60,000 crore declared by a drone study by IIT 38. Abuse of temple funds by HR&CE headed by Sekhar Babu and directing those funds to build colleges and marriage halls. 39. Crops worth several crores getting wasted 40. Abolition of TNPSC 41. Failure to appoint a full time DGP AKA Head of Police Force 42. Choking freedom of speech by witch-hunting critics – Savukku Shankar and, 43. Taking control of the film industry for "ALL" purposes possible! The current government is by far the worst Tamil Nadu has ever seen. Voters should bear this in mind when they exercise their right in May 2026. Udhayanidhi could become the CM if DMK wins again. Your future is in your hands. BTW – If I have missing any of DMK’s achievements, please let me know through your comments!

Dr. Praveen Vijaykumar

57,221 views • 7 months ago

A guy started 14 days ago posting faceless World Cup videos made with AI. He's already breaking 1M views and getting paid like it. Here's the part nobody tells you. Everyone's chasing Shorts. Shorts barely pay. You can hit 1M views and walk away with $40. Long-form is where the money sits. Fewer views, 10x the RPM, and the World Cup window is open right now for maybe 30 days before everyone floods it. And one long-form video now takes under 5 minutes to make. Phase 1: Script Go to solo and run their scriptwriter. It follows a retention framework, not random AI filler. Output a 6-8 minute script built to hold watch time past the 30% mark, which is where YouTube starts pushing you. Phase 2: Scenes Paste that script into the scene generator. Pick your shot count, lock a style, hit go. 2 minutes later you have the full visual sequence rendered. Phase 3: Voice Drop the same script into the voice studio. One clean voiceover, no mic, no face, no accent worries. Phase 4: Thumbnail Use their thumbnail maker. The thumbnail does 80% of the click work. Big number, one face, one tension. Phase 5: Assemble Stitch scenes, voice, and thumbnail in CapCut. Export. Upload. That's the whole loop. Script to upload in under 5 minutes once you've done it twice. Realistic timeline, not fantasy: Days 1-7: 1 video a day, 200-2,000 views each. Boring. Keep going. Days 8-21: one video catches the algorithm. 40K, then 300K. Days 22-40: the channel finds its lane. 1M+ becomes normal during the tournament. The trap most people fall into: they start in week 3 of the World Cup. By then the search demand peaks and the supply explodes. The edge is starting today, not after you've watched 9 more tutorials. The style works in any niche. World Cup is just the easiest demand spike on the calendar right now. I put the full step-by-step into a free walkthrough. Exact tools, exact post schedule, which video to make first, and when to start uploading so the algorithm picks you up before the final. Comment "START" and I'll send it. The tournament ends in 30 days. The window closes with it.
0:54

A guy started 14 days ago posting faceless World Cup videos made with AI. He's already breaking 1M views and getting paid like it. Here's the part nobody tells you. Everyone's chasing Shorts. Shorts barely pay. You can hit 1M views and walk away with $40. Long-form is where the money sits. Fewer views, 10x the RPM, and the World Cup window is open right now for maybe 30 days before everyone floods it. And one long-form video now takes under 5 minutes to make. Phase 1: Script Go to solo and run their scriptwriter. It follows a retention framework, not random AI filler. Output a 6-8 minute script built to hold watch time past the 30% mark, which is where YouTube starts pushing you. Phase 2: Scenes Paste that script into the scene generator. Pick your shot count, lock a style, hit go. 2 minutes later you have the full visual sequence rendered. Phase 3: Voice Drop the same script into the voice studio. One clean voiceover, no mic, no face, no accent worries. Phase 4: Thumbnail Use their thumbnail maker. The thumbnail does 80% of the click work. Big number, one face, one tension. Phase 5: Assemble Stitch scenes, voice, and thumbnail in CapCut. Export. Upload. That's the whole loop. Script to upload in under 5 minutes once you've done it twice. Realistic timeline, not fantasy: Days 1-7: 1 video a day, 200-2,000 views each. Boring. Keep going. Days 8-21: one video catches the algorithm. 40K, then 300K. Days 22-40: the channel finds its lane. 1M+ becomes normal during the tournament. The trap most people fall into: they start in week 3 of the World Cup. By then the search demand peaks and the supply explodes. The edge is starting today, not after you've watched 9 more tutorials. The style works in any niche. World Cup is just the easiest demand spike on the calendar right now. I put the full step-by-step into a free walkthrough. Exact tools, exact post schedule, which video to make first, and when to start uploading so the algorithm picks you up before the final. Comment "START" and I'll send it. The tournament ends in 30 days. The window closes with it.

Veltrx

60,092 views • 3 months ago

I'm making over $1,000 an hour with one AI offer. The entire thing runs on Claude Opus 4.8. I call it the AI Concierge. Clients pay me $1,500+ a month for two 45-minute calls where we build their AI systems live, on their screen. I have 4 clients. I'm capping at 6. Here's the entire model: 1) The intake form is the audit. A 10-minute JotForm (built by Claude) surfaces their time sinks and hands me 1-3 AI opportunities before call one. 2) Done-with-you, not done-for-you. They share their screen. We build skills, set up Cowork, and write context files together. They learn to drive. (Done-for-you is the upsell.) 3) Every session runs through AOA: Audit, Optimize, Automate. Fix the process first, then turn it into a skill. Automating chaos just gives you faster chaos. 4) Day one has to move the needle. We ship at least one skill or automation on call one. No first-call win, dead engagement. 5) Unlimited Voxer between calls. They send a voice message, I reply in under 12 business hours. A 24/7 partner, not a guy they see twice a month. 6) The Notion hub is the renewal mechanism. Every call logs a quantified list of what we built. "Call one: 2 skills, 3 context files, Cowork live" makes $1.5K a month a no-brainer. 7) I never fill Notion out by hand. Two Claude skills log the call, pull the action items, and draft the recap email. 30 seconds. 8) Pricing ladder: $1,000/month, then $1,500 at 2 clients, then $1,800. At $1,500 you're already at $1,000/hour. If everyone says yes then you're priced too low. Two things that make this work: 1) Build the fulfillment infrastructure once. An afternoon. Then it runs itself outside the calls. 2) The value must be visible. People renew what they can measure. Full breakdown below. Go watch.
15:21

I'm making over $1,000 an hour with one AI offer. The entire thing runs on Claude Opus 4.8. I call it the AI Concierge. Clients pay me $1,500+ a month for two 45-minute calls where we build their AI systems live, on their screen. I have 4 clients. I'm capping at 6. Here's the entire model: 1) The intake form is the audit. A 10-minute JotForm (built by Claude) surfaces their time sinks and hands me 1-3 AI opportunities before call one. 2) Done-with-you, not done-for-you. They share their screen. We build skills, set up Cowork, and write context files together. They learn to drive. (Done-for-you is the upsell.) 3) Every session runs through AOA: Audit, Optimize, Automate. Fix the process first, then turn it into a skill. Automating chaos just gives you faster chaos. 4) Day one has to move the needle. We ship at least one skill or automation on call one. No first-call win, dead engagement. 5) Unlimited Voxer between calls. They send a voice message, I reply in under 12 business hours. A 24/7 partner, not a guy they see twice a month. 6) The Notion hub is the renewal mechanism. Every call logs a quantified list of what we built. "Call one: 2 skills, 3 context files, Cowork live" makes $1.5K a month a no-brainer. 7) I never fill Notion out by hand. Two Claude skills log the call, pull the action items, and draft the recap email. 30 seconds. 8) Pricing ladder: $1,000/month, then $1,500 at 2 clients, then $1,800. At $1,500 you're already at $1,000/hour. If everyone says yes then you're priced too low. Two things that make this work: 1) Build the fulfillment infrastructure once. An afternoon. Then it runs itself outside the calls. 2) The value must be visible. People renew what they can measure. Full breakdown below. Go watch.

Corey Ganim

104,819 views • 4 months ago

🚨DEVASTATING NEW REVELATIONS: Police Took A FULL EIGHT MINUTES To Realise Henry Nowak Was Stabbed To Death - While He Lay Handcuffed, Dragged, And Bleeding Out Right In Front Of Them! 😡 18-year-old Southampton University student Henry Nowak was brutally stabbed FIVE times with a 21cm blade on 3 December 2025 by Vickrum Digwa (23). Digwa and his brother immediately lied to police, screaming that Henry had racially attacked them. A dying Henry pleaded with officers NINE TIMES that he couldn’t breathe and FOUR TIMES that he had been stabbed. One officer’s cold reply? “I don’t think you have, mate.” Instead of saving him, officers dragged the dying teen along the floor, slapped handcuffs on him behind his back, and read him his rights as blood poured from his wounds. Those were some of the last words Henry ever heard. NEW bodycam audio exposes the horrifying delay: MALE OFFICER: I’m not sure he’s breathing. FEMALE OFFICER: (Checks neck) MALE: He’s not breathing. FEMALE: Right, let’s get the handcuffs off. FEMALE: (On radio) From 4-8 we don’t think he’s breathing… Got no pulse. Handcuffs off. CPR begins. Minutes drag on with Henry dying in front of them: FEMALE OFFICER: Can you put a torch… I just wanna make sure that he hasn’t been stabbed. Clothing ripped open - EIGHT FULL MINUTES after police arrived. MALE OFFICER: Has he been stabbed there? FEMALE: Yeah, he’s got a stab… there’s a mark there. MALE: That makes it worse. I’m pushing on a f*cking stab wound. MALE OFFICER (whispers): He’s f*cking gone. He’s got blood coming out his nose. They were doing f*cking chest compressions directly on a fatal stab wound for minutes without even checking! Unbelievable! Paramedics arrived too late. Henry was pronounced dead at the scene at 00:37. This wasn’t a simple mistake. This was a dying White British boy, begging for help, being dismissed and treated as the criminal the moment the attacker cried “racism”. They prioritised the false narrative over the evidence right in front of them: a teenager bleeding out. This is institutional failure at its most evil, where ideology apparently overrides basic humanity and police duty. British policing is broken when “racism” accusations from the perpetrator trump a victim fighting for his life. Every officer involved should be shamed, fired, investigated, and prosecuted. The IOPC must expose how this happened.
1:07

🚨DEVASTATING NEW REVELATIONS: Police Took A FULL EIGHT MINUTES To Realise Henry Nowak Was Stabbed To Death - While He Lay Handcuffed, Dragged, And Bleeding Out Right In Front Of Them! 😡 18-year-old Southampton University student Henry Nowak was brutally stabbed FIVE times with a 21cm blade on 3 December 2025 by Vickrum Digwa (23). Digwa and his brother immediately lied to police, screaming that Henry had racially attacked them. A dying Henry pleaded with officers NINE TIMES that he couldn’t breathe and FOUR TIMES that he had been stabbed. One officer’s cold reply? “I don’t think you have, mate.” Instead of saving him, officers dragged the dying teen along the floor, slapped handcuffs on him behind his back, and read him his rights as blood poured from his wounds. Those were some of the last words Henry ever heard. NEW bodycam audio exposes the horrifying delay: MALE OFFICER: I’m not sure he’s breathing. FEMALE OFFICER: (Checks neck) MALE: He’s not breathing. FEMALE: Right, let’s get the handcuffs off. FEMALE: (On radio) From 4-8 we don’t think he’s breathing… Got no pulse. Handcuffs off. CPR begins. Minutes drag on with Henry dying in front of them: FEMALE OFFICER: Can you put a torch… I just wanna make sure that he hasn’t been stabbed. Clothing ripped open - EIGHT FULL MINUTES after police arrived. MALE OFFICER: Has he been stabbed there? FEMALE: Yeah, he’s got a stab… there’s a mark there. MALE: That makes it worse. I’m pushing on a f*cking stab wound. MALE OFFICER (whispers): He’s f*cking gone. He’s got blood coming out his nose. They were doing f*cking chest compressions directly on a fatal stab wound for minutes without even checking! Unbelievable! Paramedics arrived too late. Henry was pronounced dead at the scene at 00:37. This wasn’t a simple mistake. This was a dying White British boy, begging for help, being dismissed and treated as the criminal the moment the attacker cried “racism”. They prioritised the false narrative over the evidence right in front of them: a teenager bleeding out. This is institutional failure at its most evil, where ideology apparently overrides basic humanity and police duty. British policing is broken when “racism” accusations from the perpetrator trump a victim fighting for his life. Every officer involved should be shamed, fired, investigated, and prosecuted. The IOPC must expose how this happened.

J Stewart

12,582 views • 3 months ago

Seedance 2.5 just got an upgrade with 1080p only on Higgsfield... you can now ONE SHOT commercials like this here's exactly how to do it (with a gift at then end): 1/ write the prompt as a timestamped breakdown > split the spot second by second > put the exact dialogue inside each window in quotes, the model speaks it word for word with lip sync > match the generation length to your timestamps, a 21 second script generated at 15 compresses and the delivery desyncs 2/ composition is named - never hoped for > name the camera: "handheld front camera, chest-up framing, natural micro shakes" for UGC, "35mm, slow push-in, product centered" for a produced spot > name the light: "golden hour through the windshield" or "ring light with slight reflections in the eyes" > name the grade: "high contrast, cool tones, warm skin" > whatever you leave unstated gets invented and locked for the whole clip 3/ characters hold when you anchor them > generate a first frame image before any video: real skin texture, visible pores, one fixed imperfection like freckles so drift becomes instantly visible > feed it in as the reference and open every prompt with "the same person as the reference, identical face, hair and outfit" > the @ system locks it harder: @.character for the face, @.style for the look, @.audio for the voice > then write the micro behaviors: a glance away and back, a pre-line breath, fingers adjusting grip... small involuntary movement is what reads human, and you get it by naming it 4/ sound is written - not defaulted > end every prompt with an audio block: "clear phone-mic voice with light room tone" for UGC, "clean studio voice, no echo" for a produced spot > name the music under the dialogue: "soft upbeat synth instrumental running quietly underneath" > one delivery word for the read: "delivery: fed up" produces a performance, an adjective stack produces nothing 5/ sharp text is quoted text > write the exact label or on-screen text in quotes with its style > unquoted text gets invented typography that garbles between frames > print brand names big, a bold label survives every shot while a small tag melts 6/ the consistency laws > repeat the product description verbatim in every prompt, faces anchor but products drift > count objects scene-wide: "exactly one bottle in the entire scene, no duplicate on any surface" > close the wardrobe: "small gold studs, no other jewellery, no rings, no watch" > every state change happens across a cut: the swatch on her hand in shot one, blended in shot two, no clip contains the transition... the viewer's brain supplies it and the shortcut for UGC: take an ad that already converted, ask gemini for a 1:1 timestamped breakdown of everything on screen, swap in your product and script -> that breakdown is your prompt set 9:16 for shortform, 16:9 for the spot, generate straight at 1080p and ship RT + reply to this post and i'll send you my full guide to make your own creatives
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Seedance 2.5 just got an upgrade with 1080p only on Higgsfield... you can now ONE SHOT commercials like this here's exactly how to do it (with a gift at then end): 1/ write the prompt as a timestamped breakdown > split the spot second by second > put the exact dialogue inside each window in quotes, the model speaks it word for word with lip sync > match the generation length to your timestamps, a 21 second script generated at 15 compresses and the delivery desyncs 2/ composition is named - never hoped for > name the camera: "handheld front camera, chest-up framing, natural micro shakes" for UGC, "35mm, slow push-in, product centered" for a produced spot > name the light: "golden hour through the windshield" or "ring light with slight reflections in the eyes" > name the grade: "high contrast, cool tones, warm skin" > whatever you leave unstated gets invented and locked for the whole clip 3/ characters hold when you anchor them > generate a first frame image before any video: real skin texture, visible pores, one fixed imperfection like freckles so drift becomes instantly visible > feed it in as the reference and open every prompt with "the same person as the reference, identical face, hair and outfit" > the @ system locks it harder: @.character for the face, @.style for the look, @.audio for the voice > then write the micro behaviors: a glance away and back, a pre-line breath, fingers adjusting grip... small involuntary movement is what reads human, and you get it by naming it 4/ sound is written - not defaulted > end every prompt with an audio block: "clear phone-mic voice with light room tone" for UGC, "clean studio voice, no echo" for a produced spot > name the music under the dialogue: "soft upbeat synth instrumental running quietly underneath" > one delivery word for the read: "delivery: fed up" produces a performance, an adjective stack produces nothing 5/ sharp text is quoted text > write the exact label or on-screen text in quotes with its style > unquoted text gets invented typography that garbles between frames > print brand names big, a bold label survives every shot while a small tag melts 6/ the consistency laws > repeat the product description verbatim in every prompt, faces anchor but products drift > count objects scene-wide: "exactly one bottle in the entire scene, no duplicate on any surface" > close the wardrobe: "small gold studs, no other jewellery, no rings, no watch" > every state change happens across a cut: the swatch on her hand in shot one, blended in shot two, no clip contains the transition... the viewer's brain supplies it and the shortcut for UGC: take an ad that already converted, ask gemini for a 1:1 timestamped breakdown of everything on screen, swap in your product and script -> that breakdown is your prompt set 9:16 for shortform, 16:9 for the spot, generate straight at 1080p and ship RT + reply to this post and i'll send you my full guide to make your own creatives

Machina

12,785 views • 1 month ago

I spent 2.5 billion tokens, every limit I had and five days to make this. A GTA-style city, built with GPT-6 using only prompts. No code or 3D by hand. The whole point was to push it as far as it goes and wire in every API I had. The setup: Codex driving Unreal Engine 5, with Higgsfield, Tripo, Meshy, Seedance, Seed Audio and fal handling the buildings, characters, animations, materials and sound. 8 buildings taken from concept to game, 247 generated voice lines for residents and police, 10 radio tracks across 5 stations. Here's the honest part. Most of those tokens didn't go into building. They went into fixing. The model writes a mechanic, runs it, watches it break, and tries again for hours. Getting a character to open a car door and actually sit in the seat took an absurd number of passes. A carjacking needed two separate bodies to stay locked to the same grab. Half the work was animation that looked fine on its own and fell apart the moment it ran in the game. And almost all of that 2.5B is the same project history being re-read from cache on every request, not unique code. Procedural generation from prompts is genuinely fun to watch and wildly inefficient at the same time. The city started as glowing blocks with working driving, money and jobs underneath, and ended up somewhere I'd actually screenshot. Full breakdown:
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I spent 2.5 billion tokens, every limit I had and five days to make this. A GTA-style city, built with GPT-6 using only prompts. No code or 3D by hand. The whole point was to push it as far as it goes and wire in every API I had. The setup: Codex driving Unreal Engine 5, with Higgsfield, Tripo, Meshy, Seedance, Seed Audio and fal handling the buildings, characters, animations, materials and sound. 8 buildings taken from concept to game, 247 generated voice lines for residents and police, 10 radio tracks across 5 stations. Here's the honest part. Most of those tokens didn't go into building. They went into fixing. The model writes a mechanic, runs it, watches it break, and tries again for hours. Getting a character to open a car door and actually sit in the seat took an absurd number of passes. A carjacking needed two separate bodies to stay locked to the same grab. Half the work was animation that looked fine on its own and fell apart the moment it ran in the game. And almost all of that 2.5B is the same project history being re-read from cache on every request, not unique code. Procedural generation from prompts is genuinely fun to watch and wildly inefficient at the same time. The city started as glowing blocks with working driving, money and jobs underneath, and ended up somewhere I'd actually screenshot. Full breakdown:

Stefan 3D AI

129,162 views • 22 days ago