AI News Archive: August 12, 2026 — Part 11
Sourced from 500+ daily AI sources, scored by relevance.
- Which GIGABYTE AI Gaming Laptop Should You Buy in 2026?
Whether you’re chasing esports victories, editing videos or tackling AI-powered workflows, GIGABYTE offers an AI gaming laptop designed to match your performance needs and lifestyle.
Score: 20🌐 MovesAug 12, 2026https://www.cnet.com/paid-content/which-gigabyte-ai-gaming-laptop-should-you-buy-in-2026/ - Demon Safety
(by LemmySmackett ) "Hey man, I haven't seen you in a minute. What are you up to these days?" "Been on that grind, bro. I got a new gig." "Really? You found a job in this dog shit economy?" "Full time, full benies. And the pay is insane." "That's great to hear, man. Let's fuckin' go!" "Let's fuckin' go." "Hey, maybe you can hook me up? I'm sick of this retail bullshit." "Well—" "If I gotta stock one more shelf at CostGro, I swear to God—" "It's a competitive position. And you need a degree." "Come on. I just got my G.E.D." "That's not—" "Just tell me what you're working in, bro. Maybe I can come on as an intern." "Demon Safety." "*Demon* Safety?" "You know: fiends, pookas, yokai, boggarts—" "Wow." "The occasional cambion." "Sounds intense." "It is. But it's fulfilling work that makes the world a better place." "That's inspiring, bro." "And the pay is insane." "And you're sure they're not hiring?" "Oh, they're hiring. They're just not hiring you." "Damn." "Sorry." "So like, what, it's a security-for-hire deal? You stop demons from terrorizing people?" "Well—" "Cause if it's a bouncer gig, I got experience from the local county fair bounce house." "No, no. The opposite, actually." "Opposite?" "My job is to summon as many demons as I can, as fast as I can—" "Uh." "—and to make them as terrifying as possible—" "Uuh." "—in the name of Safety." "Right. Okay. A few questions." Continues here, by the Twitter user LemmySmackett. Discuss
- This Newton teen’s app uses AI to help people manage diabetes
This Newton teen’s app uses AI to help people manage diabetes The Boston Globe
Score: 20🌐 MovesAug 12, 2026https://www.bostonglobe.com/2026/08/12/business/diabetes-management-app-ai-aaron-prager/ - Purple Exhibits Builds Turnkey Amazon AI Classroom at Sigma Gamma Rho in Tampa
Purple Exhibits Builds Turnkey Amazon AI Classroom at Sigma Gamma Rho in Tampa USA Today
- Women urged to seize opportunities in AI revolution
As AI reshapes the workplace, women must move from being technology users to creators and 'solutionists', says Zanele Njapha, CEO and founder of The UnLearners.
Score: 20🌐 MovesAug 12, 2026https://www.itweb.co.za/article/women-urged-to-seize-opportunities-in-ai-revolution/WnxpEv4YJp87V8XL - IBV - 2026 Tech Leader Study: Redefining the tech leader's mandate report
IBV - 2026 Tech Leader Study: Redefining the tech leader's mandate report IT Pro
- This $200 AI app turns your spoken words into polished writing
Speak words into polished, professional writing with a lifetime subscription to Contextli Pro Plus Plan.
Score: 20🌐 MovesAug 12, 2026https://mashable.com/tech/aug-12-contextli-pro-plus-plan-lifetime-subscription - TestMu AI Launches One-Click Migration From qTest to Test Manager
TestMu AI Launches One-Click Migration From qTest to Test Manager Toronto Star
- Poor numeracy is a blind spot in the age of AI
An understanding of maths can be crucial to judging if the tech is telling you the right thing
- Haven Safety AI Launches HavenASSURE, an AI Platform for Investigation Quality Assurance, and Achieves SOC 2 Type II Attestation
Haven Safety AI Launches HavenASSURE, an AI Platform for Investigation Quality Assurance, and Achieves SOC 2 Type II Attestation USA Today
- Lucrative AI Launches Out of Stealth with $500K in Pre-Seed Funding to Bring MCP-Native Automation to Enterprise
Lucrative AI Launches Out of Stealth with $500K in Pre-Seed Funding to Bring MCP-Native Automation to Enterprise azcentral.com and The Arizona Republic
- Magic Engine Studios to Premiere Co-Founder, Brian James Gage’s Fifth Full-Length AI Feature Film
Magic Engine Studios to Premiere Co-Founder, Brian James Gage’s Fifth Full-Length AI Feature Film azcentral.com and The Arizona Republic
- Forum: AI should help artists tell better stories, not replace them
Forum: AI should help artists tell better stories, not replace them The Straits Times
Score: 18🌐 MovesAug 12, 2026https://www.straitstimes.com/opinion/forum/forum-ai-should-help-artists-tell-better-stories-not-replace-them?ref - Dubai startup dataHabibi launches AI platform for property intelligence
Dubai startup dataHabibi launches AI platform for property intelligence
- The boring press release machines learned to love
One quarter of weekly releases produced 1,058 AI citations, turning a marketing chore nobody wanted into the cheapest visibility in logistics The post The boring press release machines learned to love appeared first on FreightWaves .
Score: 18🌐 MovesAug 12, 2026https://www.freightwaves.com/news/the-boring-press-release-machines-learned-to-love - Simaia, an AI-Native GEO Marketing Team, Raises Pre-Seed Funding via Iterative’s S26 Batch
Simaia, an AI-Native GEO Marketing Team, Raises Pre-Seed Funding via Iterative’s S26 Batch USA Today
- HCIactive Introduces Human Experience Engineering to Make Healthcare Simpler in the Age of AI
HCIactive Introduces Human Experience Engineering to Make Healthcare Simpler in the Age of AI azcentral.com and The Arizona Republic
- These 10 'AI proof' jobs have one thing in common. See list
These 10 'AI proof' jobs have one thing in common. See list USA Today
Score: 18🌐 MovesAug 12, 2026https://www.usatoday.com/story/money/2026/08/12/10-ai-proof-jobs-resume-now-list/91271892007/ - 7 Ways to Put AI to Work in Your Agency (Without a Tech Team)
Key Takeaways AI for a small agency does not require a tech team. Most of what is useful today is either already built into your software or coming soon to the system you may already use. There are seven specific …
- Benevolve Powers AI-Driven Talent Intelligence for Digital-Native Enterprises
Benevolve is helping digital-native enterprises build agile, skills-first workforces through AI-driven talent intelligence solutions. Trusted by leading technology-first organizations including PhonePe, Myntra and MediBuddy, Benevolve enables fast-growing businesses to make data-driven talent decisions, identify critical skill gaps and build organizations equipped for the future of work. As AI continues to reshape industries and redefine job […] The post Benevolve Powers AI-Driven Talent Intelligence for Digital-Native Enterprises appeared first on CXOToday.com .
- How to Decide Chunk Size in Any Project: Complete Interview Guide
This guide covers the decision-making framework that interviewers expect you to explain, with real-world trade-offs and problem-solving approach. 🎯 Interview Opening Statement (First 30 seconds) What Interviewer Wants to Hear: "Chunk size is NOT a fixed number - it's a design decision based on multiple factors: document type, retrieval precision, token budget, LLM context window, and latency requirements. I use a systematic approach: analyze constraints → model requirements → test with metrics → iterate." 📋 THE SYSTEMATIC FRAMEWORK (Core Answer Structure) Step 1: Understand Project Constraints A. Document Type & Domain Interview Question: “How would chunk size differ for legal documents vs news articles?” Answer Template: Legal documents (1000-2000 words): - Why: A clause or section is an indivisible unit of meaning - Risk: Breaking mid-clause creates ambiguity in retrieval - Example: Contract terms must be complete to be accurate News articles (400-800 words): - Why: Readers expect paragraph-level information - Benefit: Smaller chunks allow precise topic retrieval - Trade-off: May need overlap to connect ideas B. Use Case Requirements Ask yourself these questions: 1. PRECISION REQUIREMENT □ High precision needed? → SMALLER chunks (400-600 words) Example: Legal discovery, medical diagnosis □ Moderate precision? → MEDIUM chunks (700-1200 words) Example: Technical support, FAQ systems □ Broad context OK? → LARGER chunks (1500-2500 words) Example: General knowledge Q&A 2. RETRIEVAL TYPE □ Exact match needed? → SMALLER chunks Example: "Find this specific clause" □ Semantic match? → MEDIUM chunks Example: "Find information about refunds" □ Topic match? → LARGER chunks Example: "Explain machine learning basics" 3. LATENCY REQUIREMENTS □ Real-time (< 500ms)? → SMALLER chunks (Faster embedding, faster search) □ Sub-second (< 2s)? → MEDIUM chunks □ Can tolerate delay? → LARGER chunks (More context means better answers) 4. COST SENSITIVITY □ Budget tight? → SMALLER chunks (Fewer embeddings to compute) □ Can spend? → LARGER chunks (More embeddings = better precision) Interview Answer Example: "For a chatbot answering customer support tickets, I'd analyze: - Precision: Medium-high (accuracy matters) - Retrieval: Semantic (users phrase questions differently) - Latency: < 2 seconds (acceptable for chat) - Cost: Moderate budget This suggests 600-1000 word chunks with 15% overlap." Step 2: Model & Infrastructure Constraints A. LLM Context Window # Key calculation interviewers expect: CONTEXT_BUDGET = LLM_CONTEXT_WINDOW * 0.6 # Reserve 60% for safety # Example with Claude 3 Sonnet (200K tokens) total_tokens = 200_000 safe_budget = total_tokens * 0.6 # 120,000 tokens reserved_for_prompt = 20_000 tokens reserved_for_response = 10_000 tokens available_for_context = 90_000 tokens # Now work backwards from chunks: tokens_per_1000_chars = 250 # Rough estimate available_chars = 90_000 / 250 * 1000 # ~360,000 characters # If chunk_size = 2000 chars, how many chunks can we fit? max_chunks = 360_000 / 2000 # ~180 chunks # But typically use only top-5 to top-10 chunks reasonable_budget = 10 * 2000 # 20,000 chars = 5,000 tokens Interview Answer: "Given Claude's 200K token window, I'd allocate: - 60% for context (120K tokens) - 15% for prompt (30K tokens) - 15% for response (30K tokens) - 10% safety buffer (20K tokens) If each 2000-char chunk ≈ 500 tokens, I can safely fit 10-15 chunks. Working backward: 15 chunks * 2000 chars = 30,000 chars maximum context. For better retrieval quality, I'd use parent-child chunking where top-5 most relevant child chunks expand to their parent chunks for full context." B. Embedding Model Constraints # Most embedding models have input limitations: EMBEDDING_LIMITS = { "OpenAI (text-embedding-3-large)": "8,191 tokens (~6,000 words)", "Anthropic (own embeddings)": "varies", "Cohere": "1,024 tokens (safe limit)", "Local models (ONNX)": "512-2048 tokens typically", } # This limits chunk size BEFORE embedding: # If embedding model accepts 1024 tokens max: # That's roughly 750-1000 words or 3000-4000 characters # Safe chunk size < embedding_limit CHUNK_SIZE = min(2000_chars, embedding_model_limit) Interview Question: How does embedding model capacity affect your chunk size decision? Answer: "Embedding models have token limits. For example, Cohere's model safely handles 1024 tokens. Since 1 token ≈ 4 characters: - 1024 tokens ≈ 4000 characters max Even if I want 2000-char chunks for my use case, I must verify the embedding model can handle it. If it can't, I either: 1. Use a more capable embedding model (OpenAI's 8K model) 2. Reduce chunk size (1500-2000 chars) 3. Split chunks: embed smaller, but retrieve with parent for context The embedding model is often the limiting factor." Step 3: Performance Metrics Analysis A. Key Metrics to Evaluate class ChunksizeMetrics: """ What interviewers expect you to measure: """ def __init__(self): self.metrics = { # Retrieval Quality "Precision@K": "Of top-K results, how many relevant?", "Recall@K": "Of all relevant docs, how many in top-K?", "NDCG@K": "Normalized Discounted Cumulative Gain (ranking quality)", "MRR": "Mean Reciprocal Rank (position of first relevant result)", # Cost & Performance "Embedding_Time": "Seconds to embed all chunks", "Search_Latency": "Seconds to find top-K results", "Storage_Size": "GB needed to store all embeddings", "Cost_Per_Query": "$ cost (API calls for embeddings)", # Answer Quality "Hallucination_Rate": "% of made-up information in answers", "Citation_Accuracy": "% of citations point to correct source", "Context_Coverage": "% of relevant information in context", "Token_Efficiency": "Useful info per token in context" } B. How to Present Metrics in Interview Interviewer Question: How would you evaluate if your chunk size is optimal? Answer: "I'd run experiments with 3 chunk sizes: small (400 words), medium (800 words), large (1500 words). For each, I measure: 1. RETRIEVAL QUALITY (does RAG find relevant info?) - Precision@5: "Of top 5 results, are they relevant?" - Recall@10: "Of all relevant documents, how many appear?" - Target: Precision > 0.8, Recall > 0.7 2. COST & LATENCY (can we afford it?) - Embedding cost: $ per 1M chunks - Search latency: milliseconds - Storage: GB for all embeddings - Target: <100ms latency, <1KB per chunk metadata 3. ANSWER QUALITY (does LLM generate good responses?) - Hallucination rate: Manual review of 50 samples - Citation accuracy: Does answer reference correct chunks? - Token efficiency: Useful info per token - Target: <5% hallucinations, >90% citations accurate Then I pick the size that balances these metrics best." Step 4: Empirical Testing Framework A. The A/B Testing Approach Interviewers Love class ChunkSizeExperiment: """ Structure your testing like a real data scientist """ CHUNK_SIZES = [400, 600, 800, 1000, 1500] # Words EVALUATION_SET = 100 # Test queries def run_experiment(self): results = {} for size in self.CHUNK_SIZES: # Step 1: Create chunks of this size chunks = create_chunks(text, size_words=size) # Step 2: Embed all chunks embeddings = embed_all(chunks) storage.index(embeddings) # Step 3: Run test queries metrics = { "precision_5": 0.0, "recall_10": 0.0, "latency_ms": 0.0, "cost_usd": 0.0, "hallucination_rate": 0.0, } for query in EVALUATION_SET: results_top5 = retrieve(query, k=5) # Evaluate relevance, measure latency, etc. results[size] = metrics return self.analyze_results(results) def analyze_results(self, results): """ Present findings professionally """ print(""" CHUNK SIZE ANALYSIS RESULTS ============================ Size | Precision | Recall | Latency | Cost | Halluc. ------|-----------|--------|---------|-------|-------- 400w | 0.85 | 0.72 | 45ms | $0.8 | 3.2% 600w | 0.88 | 0.78 | 50ms | $1.2 | 2.1% 800w | 0.86 | 0.82 | 55ms | $1.6 | 1.8% ⭐ BEST 1000w | 0.82 | 0.80 | 65ms | $2.0 | 1.5% 1500w | 0.79 | 0.75 | 80ms | $3.0 | 2.0% RECOMMENDATION: 800 words - Highest Recall (0.82) - Good Precision (0.86) - Reasonable latency (55ms) - Manageable cost - Lowest hallucination rate (1.8%) """) Interview Presentation: "I'd create a simple experiment with 5 chunk sizes and run 100 test queries. For each size, I measure precision, recall, latency, and cost. Based on the results, 800-word chunks provide the best balance: high recall (fewer missed docs), good precision (fewer irrelevant results), and reasonable cost. The key insight: larger chunks give better recall (more context), but smaller chunks give better precision (less noise). 800 words is the sweet spot." Step 5: Domain-Specific Considerations A. Different Industries, Different Decisions class DomainSpecificChunkSizes: """ Interview tip: Show you understand domain context """ DOMAINS = { "LEGAL": { "size": "1000-2000 words", "reason": "Sections/clauses are legal units", "example": "Contract clause must be complete", "key_metric": "Precision > Recall (accuracy critical)", "overlap": "20% (preserve clause boundaries)", }, "MEDICAL": { "size": "500-1000 words", "reason": "Patient outcomes depend on complete context", "example": "Symptoms + test results + diagnosis", "key_metric": "Recall > Precision (miss nothing)", "overlap": "20% (connect symptoms to outcomes)", }, "E-COMMERCE": { "size": "400-800 words", "reason": "Product info is naturally separated", "example": "Product specs, reviews, shipping info", "key_metric": "Speed (real-time product search)", "overlap": "10% (less critical)", }, "CUSTOMER_SUPPORT": { "size": "600-1000 words", "reason": "Q&A pairs with explanation", "example": "Question + answer + examples", "key_metric": "User satisfaction (answers must be complete)", "overlap": "15%", }, "TECHNICAL_DOCS": { "size": "500-1000 words", "reason": "API docs, parameters need to stay together", "example": "Function signature + params + examples", "key_metric": "Accuracy (wrong example breaks code)", "overlap": "15%", }, "NEWS/MEDIA": { "size": "400-600 words", "reason": "Articles are already well-written units", "example": "One news story = one natural unit", "key_metric": "Latency (real-time relevance)", "overlap": "10%", }, } Interview Answer Example: "For a medical chatbot vs an e-commerce bot, chunk sizing would be very different: MEDICAL (diagnosing symptoms): - Chunk size: 700-1000 words - Why: Symptoms, tests, diagnosis, treatment must be together - Metric: Optimize for RECALL (don't miss anything) - Overlap: 20% (very important - connect related symptoms) E-COMMERCE (product recommendation): - Chunk size: 500-800 words - Why: Products are naturally separate; specs are self-contained - Metric: Optimize for SPEED (<100ms) and cost - Overlap: 10% (less critical) The key difference: Medical prioritizes completeness; e-commerce prioritizes speed." 🔴 RED FLAGS: What NOT to Say in Interview ❌ "We just use 512-token chunks like everyone else" → Shows no independent thinking ❌ "Bigger chunks are always better" → Ignores retrieval precision vs recall trade-off ❌ "We never tested different chunk sizes" → Suggests no systematic approach ❌ "Chunk size doesn't matter much" → Shows ignorance of its impact ✅ Instead say: "We systematically tested 5 different chunk sizes on our evaluation set, measuring precision, recall, latency, and cost. Based on the results, 800-word chunks provided the best balance for our use case." 🎓 Advanced Follow-Up Questions Interviewers Ask Q1: What if your chunk size breaks an important semantic boundary? Answer: "Good question. This is where recursive chunking + parent-child hierarchy becomes critical. Approach: 1. DETECT boundaries: Identify natural sections, paragraphs, sentences 2. RECURSIVE splitting: Try to split at paragraph level first 3. FALL BACK gracefully: If paragraph > chunk_size, split by sentences 4. PRESERVE context: Use parent chunks to keep full section Example: If I want 800-word chunks but a legal clause is 1200 words: - Don't: Break the clause (loses meaning) - Do: Keep clause as one parent chunk Split into 2-3 child chunks (for embedding) Retrieve child chunks + expand to full parent (for LLM) This preserves semantic integrity while optimizing for retrieval." Q2: How would you handle variable-length documents? Answer: "Variable-length documents need adaptive chunking: 1. ANALYZE document length - If < 2000 words: Use as single chunk - If 2000-10000 words: Split into 3-5 chunks - If > 10000 words: Use hierarchical chunking 2. USE overlap intelligently - Short docs: 10% overlap (minimal redundancy) - Long docs: 20% overlap (ensure continuity) - Large docs: 25% overlap (bridge multiple chunks) 3. IMPLEMENT dynamic sizing chunks = [] if doc_length < 2000: chunks = [entire_doc] else: base_size = doc_length / (num_sections * 0.8) chunks = recursive_split(doc, size=base_size) This ensures consistency while respecting document structure." Q3: How do you handle documents with mixed content (text + tables + code)? Answer: "Mixed content requires special handling: STRATEGY: 1. DETECT content type if is_table: chunk_size = 1000_chars (tables are dense) elif is_code: chunk_size = 500_chars (code needs precision) else: chunk_size = 2000_chars (normal text) 2. KEEP units intact - Table: Never split a row across chunks - Code: Never split a function/class - Text: Split at paragraph boundary 3. ADD metadata Each chunk stores: - content_type: 'text', 'table', 'code' - importance: 'high', 'normal', 'low' - structure: original section/subsection 4. USE in retrieval Query for code → prioritize code chunks Query for stats → prioritize table chunks This ensures retrieval quality across mixed content." Q4: Your chunk size performs well in development. What happens in production with new data? Answer: "Production data often differs from dev data. I'd implement: 1. MONITORING in production - Track precision/recall on real queries - Monitor hallucination rate - Watch for queries that fail - Collect user feedback 2. A/B TESTING for changes Before changing chunk size, run experiment: - Control: Current chunk size (95% traffic) - Test: New chunk size (5% traffic) - Measure: Impact on user satisfaction, metrics 3. GRADUAL ROLLOUT - Week 1: Test new size with 5% queries - Week 2: Expand to 20% if metrics good - Week 3: Full rollout or rollback 4. FALLBACK PLAN If new size performs worse: - Immediately revert to previous size - Investigate issue - Re-test with adjusted size This ensures production stability while allowing optimization." 📊 Decision Matrix (Interview Cheat Sheet) Print this before your interview! Interviewers love when you reference a framework. CHUNK SIZE DECISION MATRIX ========================== SMALL (400-600w) | MEDIUM (800-1200w) | LARGE (1500-2500w) Precision/Recall Trade | High Precision | Balanced | High Recall Latency | Fast (<50ms) | Moderate (50-80ms) | Slow (>80ms) Cost | Low | Medium | High Semantic Preservation | At Risk | Good | Excellent Embedding Model Load | Low | Medium | High LLM Context Budget | Large margin | Comfortable | Tight Best For | E-commerce | Support/FAQ | Legal/Medical | News | Tech docs | Research YOUR USE CASE: - Domain: _______ - Priority: Speed? Cost? Accuracy? - Document length: Short / Medium / Long - Precision vs Recall: Which matters more? RECOMMENDED SIZE: _______ words 💡 Final Interview Closing (Strong Finish) When asked “How do you decide chunk size?”, end with this: "In summary, it's a three-step process: 1. ANALYZE constraints - Document type (legal vs news vs code) - Precision vs recall needs - Latency and cost budgets - Model capabilities 2. EMPIRICAL testing - Test 3-5 different sizes - Measure precision, recall, latency, cost - Evaluate hallucination rate - Select size that balances trade-offs 3. PRODUCTION monitoring - Track real-world metrics - Run A/B tests before changes - Implement fallback plan - Iterate based on user feedback The key insight: There's no universal answer. It's a design decision based on YOUR specific constraints and priorities. For this project, I would recommend [size] words because [specific reasons related to their domain]." 📚 Resources to Mention in Interview “I’ve researched this topic through:” Industry papers on retrieval-augmented generation LLM context window optimization studies A/B testing results from production RAG systems Benchmarks on embedding model capabilities Real-world case studies (legal tech, healthcare, etc.) How to Decide Chunk Size in Any Project: Complete Interview Guide was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Tells.co Brings SMS And AI Contact Strategy to Contact.io
Tells.co Brings SMS And AI Contact Strategy to Contact.io azcentral.com and The Arizona Republic
Score: 18🌐 MovesAug 12, 2026https://www.azcentral.com/press-release/story/108801/tells-co-brings-sms-and-ai-contact-strategy-to-contact-io/ - Deliver modern digital experiences, support hybrid work, and generate value from AI
Deliver modern digital experiences, support hybrid work, and generate value from AI IT Pro
- FOBI AI Inc. Announces Reinstatement to Trading on TSXV Commencing August 13, 2026
FOBI AI Inc. Announces Reinstatement to Trading on TSXV Commencing August 13, 2026 Toronto Star
- Google team tells applicants its HR filters are unreliable
Google team tells applicants its HR filters are unreliable The Straits Times
Score: 15🌐 MovesAug 12, 2026https://www.straitstimes.com/business/google-team-tells-applicants-its-hr-filters-are-unreliable - MSc in Law, Governance and AI | Faculty of Law
MSc in Law, Governance and AI | Faculty of Law University of Oxford
- PODCAST: Banking on CoreWeave
PODCAST: Banking on CoreWeave reuters.com
Score: 15🌐 MovesAug 12, 2026https://www.reuters.com/podcasts/reuters-morning-bid/banking-coreweave-2026-08-12/ - StrikePlagiarism Highlights Growing Demand for AI Detection at LACCEI 2026
StrikePlagiarism Highlights Growing Demand for AI Detection at LACCEI 2026 azcentral.com and The Arizona Republic
- Aura AI Photonics ETF Overview (PHOX)
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- Transcribe Whole Meetings With This AI Dictation Tool, $199 for Life
Contextli is an AI-powered dictation tool that works in 99 different languages, and it's only $199 for life. The post Transcribe Whole Meetings With This AI Dictation Tool, $199 for Life appeared first on TechRepublic .
- Mammotion LUBA 3 robot mower falls $850 to a record-low price
Amazon cuts the Mammotion LUBA 3 AWD 3000H to an all-time low of $2,448, down 26% from its $3,299 RRP.
Score: 15🌐 MovesAug 12, 2026https://www.androidauthority.com/mammotion-luba-3-robot-mower-deal-3697931/ - QBS Software Africa partners with Smartsheet to bring intelligent work management to Africa
QBS Software Africa will work with its channel partners to introduce Smartsheet to companies looking to improve how they plan, manage and deliver work across their teams.
- NFL record projections 2026: AI makes win-loss picks for all 32 teams
NFL record projections 2026: AI makes win-loss picks for all 32 teams USA Today
Score: 15🌐 MovesAug 12, 2026https://www.usatoday.com/story/sports/nfl/2026/08/11/nfl-record-projections-2026-ai-picks/91226845007/ - DarkCarz Introduces AI-Enabled Features Designed to Personalize Luxury Ground Transportation
DarkCarz Introduces AI-Enabled Features Designed to Personalize Luxury Ground Transportation USA Today
- See Robots in action at China’s exhibitions and retail centers
See Robots in action at China’s exhibitions and retail centers USA Today
Score: 15🌐 MovesAug 12, 2026https://www.usatoday.com/picture-gallery/tech/2026/08/12/robots-in-action-photos/91274947007/ - 'Chaos to Creation' Reaches Bestseller Status, Exploring the Future of AI, Biotechnology, and Regenerative Innovation
'Chaos to Creation' Reaches Bestseller Status, Exploring the Future of AI, Biotechnology, and Regenerative Innovation azcentral.com and The Arizona Republic
- DataMeds AI Provides Update on Corexa Health’s Pharmacy Division
DataMeds AI Provides Update on Corexa Health’s Pharmacy Division USA Today
- Cyber Resilience Insights
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- ET AI Hackathon 2.0 Phase-2 results are out: Shortlisted teams now head to the grand finale
The Phase-2 results of ET AI Hackathon 2.0 are now live, bringing the competition into its final stage. After evaluating working AI prototypes for technical strength, innovation and real-world relevance, the shortlisted teams will now present their solutions before an expert panel at the Grand Finale in Hyderabad on August 25, 2026.
- Unlearn or expire: Why unlearning speed matters more than learning speed
What is the one habit that got you here that is quietly killing you right now? I put that question to a room full of founders at the EU-Startups Summit in Malta this May, and I could see it land. Every founder in that room had a habit they were proud of – a personal […] The post Unlearn or expire: Why unlearning speed matters more than learning speed appeared first on EU-Startups .
Score: 12🌐 MovesAug 12, 2026https://www.eu-startups.com/2026/08/unlearn-or-expire-why-unlearning-speed-matters-more-than-learning-speed/ - Deputy Team Director Masaaki Komatsu of the AI Medical Engineering Team Receives Outstanding General Presentation Award at the JMAI 8th Annual Meeting
A research group led by Masaaki Komatsu, Deputy Team Director of the AI Medical Engineering Team, received the Excellent General Presentation Award for their research presentation at the 8th Annual Meeting of the Japanese Association for Medical A
- AI's forgotten hero
Without the network, there is no AI, says Anthony Laing, GM of the Networking Business Unit at NEC XON.
- Ankit Maheshwari: AI, architecture and impact
Ankit Maheshwari: AI, architecture and impact YourStory.com
Score: 10🌐 MovesAug 12, 2026https://yourstory.com/2026/08/ankit-maheshwari-ai-architecture-and-impact - This Quick Course Builds Your AI Confidence for Just $15
This Quick Course Builds Your AI Confidence for Just $15 PCMag
Score: 10🌐 MovesAug 12, 2026https://www.pcmag.com/deals/this-quick-course-builds-your-ai-confidence-for-just-15 - CQT Media and Publishing Releases 'Heavy With Joy: From An AI Adoptee, Prompted And Edited By A Real Adoptee'
CQT Media and Publishing Releases 'Heavy With Joy: From An AI Adoptee, Prompted And Edited By A Real Adoptee' azcentral.com and The Arizona Republic
- Sharks AV2511AE AI Ultra robot vacuum gets a massive 50% price cut at Amazon — save almost $300
As of Aug. 12, the Shark AV2511AE AI Ultra robot vacuum is on sale for $299.99 at Amazon, 50% off its list price of $599.
- AfricurityAI Founder Stephen D. Pullum Named to Forttuna Global 100: The Power List 2026
AfricurityAI Founder Stephen D. Pullum Named to Forttuna Global 100: The Power List 2026 USA Today
- CTR Media Network Expands to 13 Channels and 50+ Free Programs, Embracing AI Innovation
CTR Media Network Expands to 13 Channels and 50+ Free Programs, Embracing AI Innovation azcentral.com and The Arizona Republic
- TourConnect AI Named Finalist in 2026 Skift IDEA Awards
TourConnect AI Named Finalist in 2026 Skift IDEA Awards azcentral.com and The Arizona Republic
Score: 08🌐 MovesAug 12, 2026https://www.azcentral.com/press-release/story/108787/tourconnect-ai-named-finalist-in-2026-skift-idea-awards/ - Cornell researchers found a way to spot future hits before they become popular
Cornell researchers built new datasets showing that early downloads and GitHub activity can predict a paper's impact years before citations ever catch up.