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“I’ll send notes after the call.” (They didn’t.) So we built an agent that does it automatically. Before a call, it pulls together the docs, decision log, and last week’s notes. After the meeting, it writes the recap and drops it in Slack. Pull up a chair. Sam H... show more

Notion

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16,474 次观看 • 2 个月前 •via X (Twitter)

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This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?

This is insane. An AI agent can run every boring job in outbound. We spent the last 6 months building ours. Here are my 8 favorite agents to build: Replies get sorted before we open the inbox. Campaigns go live from one command. Weak inboxes pull themselves out before they hurt a domain. Here are the agents behind it: 1. Reply Agent Reads every reply and drafts the response. A human reviews, edits, and sends. 2. Mailbox Health Agent Watches inbox and domain health. It predicts when you need new mailboxes, then buys and warms them. 3. Campaign Optimizer Agent Checks every live campaign every 6 hours. If 500+ leads were emailed and replies are under 4%, it tests new copy, replaces inboxes replying under 1%, and shifts sending to better hours. 4. Lead Qualification Agent Scores each lead by company size, industry, and tech stack. It enriches the record, updates your CRM, and only loads qualified leads into campaigns. High-priority prospect? You get a Slack ping. 5. Meeting Booking Agent Finds meeting requests inside replies. It books the slot, writes prep notes using the lead's background, sends reminders, and logs the outcome. 6. Pipeline Progression Agent Tracks opens, clicks, and website visits. It moves the CRM stage, triggers the next sequence, and creates a task when a lead shows real intent. 7. Copywriting Agent Writes cold emails and follow-ups in the campaign's voice. 8. Analytics Agent Watches campaign metrics in real time and explains what to fix in plain language. We built ours with custom code. Smartlead's SmartAgents let you build agents like these from a plain-English prompt, inside the platform where your campaigns already run. If you repeat an outbound task more than twice a week, that is an agent you have not built yet. Which one would you build first?

Hosun Chung

156,050 次观看 • 23 天前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 次观看 • 2 个月前

For anyone constantly stuck in back-to-back meetings, Notion can automatically do all the follow ups. Just use AI meeting notes. Format a skill, hook it up to Notion mail, and every meeting after that handles itself.

For anyone constantly stuck in back-to-back meetings, Notion can automatically do all the follow ups. Just use AI meeting notes. Format a skill, hook it up to Notion mail, and every meeting after that handles itself.

Notion

46,431 次观看 • 4 个月前

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 次观看 • 2 个月前

you’re sitting on a goldmine of content - it’s just trapped inside your sales calls this AI agent pulls the transcript and spits out: - a blog post - a LinkedIn post - a Twitter thread - an Instagram caption all in your tone. all platform-native. all actually good. just drop in the call and get 4 ready-to-post pieces in under 60 seconds built the whole thing in n8n after realizing I kept saying great stuff on calls… but never using it want the full workflow? comment “CALL,” follow, + repost and I’ll send it over (must be following so I can DM)

you’re sitting on a goldmine of content - it’s just trapped inside your sales calls this AI agent pulls the transcript and spits out: - a blog post - a LinkedIn post - a Twitter thread - an Instagram caption all in your tone. all platform-native. all actually good. just drop in the call and get 4 ready-to-post pieces in under 60 seconds built the whole thing in n8n after realizing I kept saying great stuff on calls… but never using it want the full workflow? comment “CALL,” follow, + repost and I’ll send it over (must be following so I can DM)

J.B.

22,537 次观看 • 1 年前

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

A friend of mine works in market research Her job involves calling different brands, asking questions, and collecting insights that later turn into reports. She needs to capture everything accurately while the conversation is still happening Earlier, she used to note everything down manually during the call. It was stressful and she often had to spend extra time later cleaning up messy notes. Now she does something much simpler. She keeps the call on speaker on her laptop, turns on the Typeless mic, and it converts the conversation into clean notes in real time. After the call, she just asks it to turn those notes into a proper summary or email for her team. No scrambling through rough notes. No spending another 20 minutes rewriting everything. She even does the same thing on mobile when she needs to send a quick update after a call. What makes it powerful is how it handles real speech. We don’t talk in perfect sentences. We pause, repeat ourselves, change direction mid-sentence, and add filler words. Typeless understands that. It removes filler words, fixes repetitions, catches mid-sentence changes, and turns messy speech into clean, ready to send text, whether that’s an email, a bullet list, notes, or a message. One feature that really stands out is voice based editing. You can refine sentences, change the tone, or tweak wording just by speaking again. No typing needed. It works anywhere you write WhatsApp, Slack, email, Notes, even ChatGPT and supports 100+ languages with a personal dictionary. Privacy is also a big focus. The platform is HIPAA compliant and GDPR compliant, with zero cloud data retention. Your data is never used to train AI models, and your history stays stored locally on your device. For professionals working with sensitive information, that matters a lot. They’re also working toward SOC 2 Type II and ISO 27001 compliance. Speaking your thoughts and getting polished writing instantly is honestly much faster than typing everything. That’s basically what Typeless does. Try it here: Android: (check the video 👇) Trust center:

aditii

59,829 次观看 • 5 个月前

I just built my second brain with Hermes Agent. It remembers people, companies, projects, and decisions. It pulls from meeting notes, docs, anything I feed it, and cites the source every time. It's this repo called Gbrain, open-sourced by Y Combinator president Garry Tan. install it by pasting this into Hermes: "Install Gbrain following the instructions at Set up the embedding provider, then run gbrain init and gbrain doctor to verify." Add the MCP server so Hermes can call it directly: "Add the Gbrain MCP server and reload." The graph builds itself. Pattern matching on the people and companies already in your notes. 31 points more precise than plain vector search, by their own benchmark. The data from the video is made up so I could show you how it works. Hermes' memory remembers our conversations. Gbrain remembers my world. Open source. Runs local, no server, no Docker.

I just built my second brain with Hermes Agent. It remembers people, companies, projects, and decisions. It pulls from meeting notes, docs, anything I feed it, and cites the source every time. It's this repo called Gbrain, open-sourced by Y Combinator president Garry Tan. install it by pasting this into Hermes: "Install Gbrain following the instructions at Set up the embedding provider, then run gbrain init and gbrain doctor to verify." Add the MCP server so Hermes can call it directly: "Add the Gbrain MCP server and reload." The graph builds itself. Pattern matching on the people and companies already in your notes. 31 points more precise than plain vector search, by their own benchmark. The data from the video is made up so I could show you how it works. Hermes' memory remembers our conversations. Gbrain remembers my world. Open source. Runs local, no server, no Docker.

Alvaro Cintas

16,710 次观看 • 29 天前

We've just shipped private, automated meeting notes on your Mac, without a bot on the call. June detects meetings and takes notes for you. Turns it into decisions, action items, and follow ups. It all stays private. Stored locally. Open Source. MIT. Try it for free.

We've just shipped private, automated meeting notes on your Mac, without a bot on the call. June detects meetings and takes notes for you. Turns it into decisions, action items, and follow ups. It all stays private. Stored locally. Open Source. MIT. Try it for free.

OpenSoftware

334,788 次观看 • 1 个月前

I built a simple internal chatbot that answers your team's SOPs and FAQs using your existing Google Drive docs in the walkthrough, I show: – how it finds the right doc for any question – how your team can “@” specific files (like in Slack) – how to set it up and share it with your team in minutes reply “chatbot” and I’ll send the full video + code (must be following)

I built a simple internal chatbot that answers your team's SOPs and FAQs using your existing Google Drive docs in the walkthrough, I show: – how it finds the right doc for any question – how your team can “@” specific files (like in Slack) – how to set it up and share it with your team in minutes reply “chatbot” and I’ll send the full video + code (must be following)

Tyler

27,274 次观看 • 1 年前

A senior data scientist on call, 24/7. That's the dream. Well, these three built it. This is Data Scout, the agent that connects to Snowflake, Notion, Slack, Github, digs into your data, and sends recurring reports automatically. Honestly, one of our most-used agents at Notion. You can build one too.

A senior data scientist on call, 24/7. That's the dream. Well, these three built it. This is Data Scout, the agent that connects to Snowflake, Notion, Slack, Github, digs into your data, and sends recurring reports automatically. Honestly, one of our most-used agents at Notion. You can build one too.

Notion

37,348 次观看 • 3 个月前

"You don't call it in preseason, you don't call it in the regular season, you definitely don't call it in a final." 😳 Jimmy Conrad and Mike Grella have major doubts on the decisive penalty decision that led to Nashville's winning goal 👀

"You don't call it in preseason, you don't call it in the regular season, you definitely don't call it in a final." 😳 Jimmy Conrad and Mike Grella have major doubts on the decisive penalty decision that led to Nashville's winning goal 👀

Golazo America

110,364 次观看 • 10 个月前

the best way to find ad ideas, is to scroll TikTok, but nobody wants to do that for hours on end... so I built an AI agent that can do it for you, completely hands-off just give it a keyword and it: – scrapes the top-performing TikToks – pulls the pain points, trigger events, and emotional hooks – rewrites the viral script for my product or service want the full n8n flow + set up guide? like + comment “script” and I’ll send it over (must be following so I can DM)

the best way to find ad ideas, is to scroll TikTok, but nobody wants to do that for hours on end... so I built an AI agent that can do it for you, completely hands-off just give it a keyword and it: – scrapes the top-performing TikToks – pulls the pain points, trigger events, and emotional hooks – rewrites the viral script for my product or service want the full n8n flow + set up guide? like + comment “script” and I’ll send it over (must be following so I can DM)

J.B.

134,786 次观看 • 1 年前

Mobile money agent left devastated after an unidentified man who allegedly came in to perform a transaction swapped bundles of GH¢200 notes totaling GH¢10,000 to bundles of GH¢1 cedi notes. According to reports, the customer interrupted a transaction, claiming they couldn't reach the recipient and asked the agent to stop. Moments after the agent refunded the cash, the customer allegedly returned with the money wrapped in the same black rubber. Believing it was the same cash she had already counted, the agent accepted it without conducting another verification and proceeded with the transaction. [🎥: MoMAG]

Mobile money agent left devastated after an unidentified man who allegedly came in to perform a transaction swapped bundles of GH¢200 notes totaling GH¢10,000 to bundles of GH¢1 cedi notes. According to reports, the customer interrupted a transaction, claiming they couldn't reach the recipient and asked the agent to stop. Moments after the agent refunded the cash, the customer allegedly returned with the money wrapped in the same black rubber. Believing it was the same cash she had already counted, the agent accepted it without conducting another verification and proceeded with the transaction. [🎥: MoMAG]

SIKAOFFICIAL🦍

388,898 次观看 • 29 天前

HERMES + ELEVENLABS TURNED A $3,000/MO ANSWERING SERVICE INTO A ~$145/MO AI THAT NEVER MISSES A CALL. A missed call after 6pm isn't a missed call. It's a customer booking with your competitor. Most businesses pay $2,000-$4,000/mo for a human to make sure that doesn't happen. Last week I gave my Hermes Agent a real phone number instead. It answers every call, 24/7, in a natural ElevenLabs voice. It doesn't read a script - it checks the calendar, recognizes the caller, and books the appointment mid-call. The math on one dental clinic: → Old answering service: ~$2,400/mo (messages only) → Hermes + ElevenLabs: ~$145/mo (books in real time) → ~$27,000/yr saved per location And it's strictly better - it books at 11pm instead of leaving a voicemail. The setup is one afternoon: → Turn on Hermes' API server → Tunnel it with ngrok → Point an ElevenLabs agent at it (Custom LLM) → Attach a Twilio number Reply "ElevenLabs" + RT, and I’ll send you the detailed playbook via DM Full step-by-step build 👇

HERMES + ELEVENLABS TURNED A $3,000/MO ANSWERING SERVICE INTO A ~$145/MO AI THAT NEVER MISSES A CALL. A missed call after 6pm isn't a missed call. It's a customer booking with your competitor. Most businesses pay $2,000-$4,000/mo for a human to make sure that doesn't happen. Last week I gave my Hermes Agent a real phone number instead. It answers every call, 24/7, in a natural ElevenLabs voice. It doesn't read a script - it checks the calendar, recognizes the caller, and books the appointment mid-call. The math on one dental clinic: → Old answering service: ~$2,400/mo (messages only) → Hermes + ElevenLabs: ~$145/mo (books in real time) → ~$27,000/yr saved per location And it's strictly better - it books at 11pm instead of leaving a voicemail. The setup is one afternoon: → Turn on Hermes' API server → Tunnel it with ngrok → Point an ElevenLabs agent at it (Custom LLM) → Attach a Twilio number Reply "ElevenLabs" + RT, and I’ll send you the detailed playbook via DM Full step-by-step build 👇

ZEUS⚡️

10,383 次观看 • 2 个月前

42 AI agents now run an entire sales department. Not one of them takes a salary. Hundreds of thousands in payroll. Gone in a weekend. At the top sits the chief sales officer. It delegates to every team’s manager. Outreach writes the emails, runs voice, fires SMS across 8 languages. RevOps wires HubSpot into the data warehouse. Research scores every lead and preps every call before a rep walks in. Then the part that breaks your brain. They talk to each other. The researcher hands context to the email writer. The call analyst feeds the follow up drafter. 42 specialists. 1 nervous system. This isn’t a chatbot. It’s an org chart that compounds while I sleep. And it’s day 1. Over the next 30 days this becomes a full company. Every department. Every role. Nobody on payroll. The last team I’ll ever have to hire. Want the full breakdown of all 42 agents and the org chart? Comment 42 and I’ll send it through.

42 AI agents now run an entire sales department. Not one of them takes a salary. Hundreds of thousands in payroll. Gone in a weekend. At the top sits the chief sales officer. It delegates to every team’s manager. Outreach writes the emails, runs voice, fires SMS across 8 languages. RevOps wires HubSpot into the data warehouse. Research scores every lead and preps every call before a rep walks in. Then the part that breaks your brain. They talk to each other. The researcher hands context to the email writer. The call analyst feeds the follow up drafter. 42 specialists. 1 nervous system. This isn’t a chatbot. It’s an org chart that compounds while I sleep. And it’s day 1. Over the next 30 days this becomes a full company. Every department. Every role. Nobody on payroll. The last team I’ll ever have to hire. Want the full breakdown of all 42 agents and the org chart? Comment 42 and I’ll send it through.

West Lord

29,179 次观看 • 2 个月前

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,409 次观看 • 1 个月前

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

KingWilliam

15,099 次观看 • 22 天前

One more point on the illegal motion. One of the complaints is that it took 1:20 to call the penalty. Yes after discussion. But :30 after the touchdown is when Collinsworth says they’re meeting to talk about it. That’s the first time they mention it. Meaning they were already coming together to discuss this before the booth told us this. Now if the alert came from NY is a different question. But that would have to be an immediate notification from NY to the crew considering they started meeting after the play concluded

One more point on the illegal motion. One of the complaints is that it took 1:20 to call the penalty. Yes after discussion. But :30 after the touchdown is when Collinsworth says they’re meeting to talk about it. That’s the first time they mention it. Meaning they were already coming together to discuss this before the booth told us this. Now if the alert came from NY is a different question. But that would have to be an immediate notification from NY to the crew considering they started meeting after the play concluded

Jason Anderson

145,192 次观看 • 10 个月前

The batter tried to call time but it wasn’t granted, so keep that in mind when you make your decision. After the batter hits the ball and he runs to first base, he seems to do a kicking motion in the back of the first basemen leg. Do you think it was accidental from just running through the base, or was it on purpose?

The batter tried to call time but it wasn’t granted, so keep that in mind when you make your decision. After the batter hits the ball and he runs to first base, he seems to do a kicking motion in the back of the first basemen leg. Do you think it was accidental from just running through the base, or was it on purpose?

BaseballHistoryNut

300,011 次观看 • 2 个月前