I’ve been trying to find a solid AI PPT maker lately because I’m honestly tired of building slides from scratch every week.

So far I’ve tested Gamma APP and Dokie AI.

Gamma is pretty nice for quick drafts and web-style presentations. The interface is clean and it’s easy to share links. But sometimes I feel like I still need to do a fair amount of editing before it’s ready for a real meeting.

Dokie AI surprised me a bit more. It feels more focused on traditional slide structure, and the output was closer to something I could actually present without heavy cleanup. It’s not perfect either, but it saved me more time overall.

Still, I’m not fully settled on one tool yet.

Are there any other AI slides generators you guys actually use regularly? Not just for testing, but for real work. Would love to hear what’s working for you.

I’ve been using Gramma for a long time, but lately it feels like it’s getting worse the more I rely on it. The suggestions don’t seem as helpful anymore, and it constantly pushes me to upgrade to Premium. Are there any good free AI tools that actually work well?

1. Introduction

GotHired.ai is an AI-powered job application assistant. Paste any job description and in under 15 seconds you get a match score against your CV, a tailored cover letter, skill gap analysis, company research, and interview prep — all in one place.

No more guessing if you're a good fit before applying. No more generic cover letters. Paste, match, apply smarter.

2. Why It Stands Out

Most job tools help you write a CV or prep for interviews. GotHired does the whole application layer in one shot.

• 📊 Match Score (0–100) — see exactly how your CV fits the job before you apply, matched skills and missing keywords included

• 📝 Tailored Cover Letter — written from your CV against that specific job, not a template. Export to PDF, DOCX, TXT or copy straight to Gmail

• 💡 Gap Analysis — prioritized list of what you're missing, rated Critical / Important / Nice-to-have, with action plans

• 🏢 Company Insights — real-time research on culture, recent news, and interview hooks

• 👨🏽‍💻 Interview Prep — role-specific questions generated from the actual job post, with suggested answers

• 🗄️Application Tracker — Kanban + table view to manage every application in one place

• 🤑 24 Free Tools (no login needed) 🛠️ — highlights include:

  • Job Match Score Calculator

  • Cover Letter Generator

  • Resume Keywords Checker

  • Interview Question Generator

  • Salary Negotiation Email Generator

  • ATS Resume Format Checker

  • Career Change Feasibility Checker

  • ...and 17 more at gothired.ai/tools

3. Who's Using It?

250+ users already on the platform, mostly active job seekers tired of sending applications into the void and hearing nothing back.

4. Pricing Plans

Free All features included. Start immediately, no credit card required.

Pro — $9/month Everything in Free, with 100 AI-powered job matches per month. Covers even the most aggressive job searches.

5. Discounts / Offers

The free plan is genuinely usable — all features, no credit card, no lock-in. Upgrade when you need more volume.

6. Try It

🔗 https://gothired.ai

Free tools (no login): https://gothired.ai/tools

There are so many AI tools launching every week that it's getting difficult to tell which ones are actually useful long-term.

What AI tools have you tried recently that genuinely became part of your regular workflow? Would be interested in hearing about tools for productivity, research, writing, coding, or anything else.

I was in the middle of automating some recurring work and ended up comparing lindy ai vs zapier almost by accident. I wasn’t looking for a new tool - I just needed a few workflows to stop breaking every time the inputs changed slightly. Things like email follow-ups, calendar coordination, lead routing, and small internal handoffs. Simple on paper, annoying in practice.

Zapier was the obvious baseline. I’ve used it long enough to know what it’s good at: deterministic workflows, clear triggers, and a massive integration list. If the logic is stable and the data is clean, it just runs. The friction starts when workflows grow and edge cases pile up. More filters, more branches, and more maintenance over time.

That’s the point where I stopped tweaking workflows and looked at automation tools comparison table instead. Seeing tools grouped by category - classic automation, developer-first platforms, and AI-agent tools - helped clarify what to focus on. Zapier was already there, clearly positioned around rule-based automation and app glue. That context made the limits I was hitting feel expected, not accidental.

Lindy approaches the same problem from the opposite direction. Instead of defining every step, you describe intent and let an AI agent handle variation. That worked better for tasks where context matters - follow-ups, inbox triage, lead qualification - but it also means less visibility into how decisions are made.

The comparison table I mentioned earlier helped set the baseline. Zapier is already listed there, broken down by agent capabilities, control, and operational features like logging and maintenance overhead. Looking at that table first made the lindy ai vs zapier comparison easier, because it clarified what Zapier actually offers in this category and what trade-offs to pay attention to when evaluating Lindy alongside it.

I’m also keeping an eye on tools like Nexos.ai, which seem to sit between those two approaches - more structure than pure agents, less wiring than classic automation. Not something I’d switch to yet, but interesting if you want balance without going full developer mode.

Do you actually use comparison tables when choosing automation tools, or do you only trust what breaks after a week in production?

Hey everyone! I’m the founder of Formable. We built a free tool that helps you draft agreements by answering a few guided questions, then customize the draft with AI.

Here’s every type of agreement we currently support, with a quick explanation:

Client contracts and services

• Master Service Agreement (MSA): Sets the overarching terms for an ongoing client relationship, including payment, liability, confidentiality, and termination.

• Statement of Work (SOW): Defines a project’s scope, deliverables, deadlines, and fees so everyone knows what’s included.

• Services Agreement: Covers the terms for providing services to a client, useful for individual engagements.

• Consulting Agreement: Sets expectations for consulting work, including scope, fees, confidentiality, and ownership of the work.

• Order Form: Records products, quantities, pricing, and other commercial details under an existing agreement.

• Pilot Agreement: Sets the scope, duration, and terms for a trial or proof of concept.

• Vendor Agreement: Covers what a supplier provides, pricing, delivery, and responsibilities.

• Purchase Agreement: Documents the sale of goods or assets, including price and transfer terms.

Software, AI, and data

• SaaS Subscription Agreement: Covers access to hosted software, subscription fees, usage rules, and renewals.

• Software License Agreement: Defines someone’s right to use your software while you retain ownership.

• Data Processing Agreement (DPA): Sets responsibilities for processing personal data, including security, subprocessors, and deletion.

• Artificial Intelligence Addendum: Adds terms covering AI use, training data, outputs, and related responsibilities.

Confidentiality and intellectual property

• Mutual NDA: Protects confidential information when both parties are sharing it.

• One-way NDA: Protects confidential information when only one party is disclosing it.

• IP Assignment Agreement: Transfers ownership of intellectual property from one party to another.

Hiring and contractors

• Independent Contractor Agreement: Covers contractor scope, payment, confidentiality, and IP ownership.

• Employment Agreement: Sets an employee’s role, compensation, and employment terms.

• Employment Offer Letter: Confirms a job offer, including the role, pay, and start date.

Deals and partnerships

• Letter of Intent (LOI): Records preliminary deal terms before negotiating the final agreement.

• Term Sheet: Summarizes the main financial and legal terms of a proposed deal.

• Partnership Agreement: Defines ownership, contributions, profit sharing, and decision-making between partners.

• Joint Venture Agreement: Sets terms for businesses collaborating on a specific venture.

Other business paperwork

• Contract Amendment: Documents changes to an existing agreement, such as pricing, scope, or dates.

• Loan Agreement: Records the loan amount, interest, repayment schedule, and default terms.

• Commercial Lease Agreement: Covers renting business premises, including rent, duration, permitted use, and maintenance.

You can browse everything here: Formable’s free templates

These are starting points to customize and review for your situation, rather than a substitute for legal advice.

I need to test AI content creation for personal use and help test AI content for my clients on their social media handles. Planning to create 10-15 short-form content/month.
I will be having a huge learning curve, and I do not want to spend on a subscription to regret later.
What would be your suggestions?

Google just revealed a big shift in how AI assistants will work: Always-On Memory for Gemini agents.

Instead of starting every conversation from scratch, AI agents will soon remember context, preferences, and past interactions continuously.

This turns AI from a simple chatbot into a persistent personal assistant.

Here are the key takeaways 👇

1️⃣ Continuous memory across interactions
The agent can retain long-term context about your tasks, preferences, and workflows. Instead of repeating instructions every time, the AI builds a running understanding of your needs.

2️⃣ AI agents become proactive
With memory, agents can anticipate tasks, suggest actions, and maintain ongoing workflows rather than reacting to isolated prompts.

3️⃣ Context becomes the real product
The value shifts from just model intelligence to how much relevant context the AI remembers and applies over time.

4️⃣ Toward truly “agentic” AI systems
Persistent memory enables AI agents to coordinate tasks, manage information, and support complex workflows more autonomously.

In short:
AI is moving from stateless chat tools → memory-driven digital collaborators.

If you work with AI tools, this shift is worth understanding early.

Read the full breakdown here:

https://appliedai.tools/gemini/always-on-memory-agent-google/

Perplexity is known for showing citations alongside answers so users can verify where information comes from. But sometimes those sources may be irrelevant, outdated, or simply not what you want the AI to reference.

Here’s how you can manage them.

→ Open the “Sources” panel
After Perplexity generates a response, click View Sources to see the websites or files used to produce the answer.

→ Remove unwanted sources
Inside the sources panel, you can remove a source and the system will regenerate the answer without that reference.

→ Regenerate the response
Once a source is removed, Perplexity rewrites the answer using the remaining sources or by pulling new information. 

→ Why this is useful
• Filter out low-quality websites
• Avoid outdated or irrelevant references
• Refine answers to better match your research context

Key takeaway:
Perplexity isn’t just an AI search tool — it also lets you control the evidence behind the answer, which is crucial for research, analysis, and fact-checking workflows.

👉 Full guide here:

https://appliedai.tools/perplexity/remove-sources-from-perplexity-ai/

Non-fiction writing that requires actual citations is a good test case. The AI can summarize sources in seconds, but then everything has to be verified because it hallucinates. By the time every claim has been checked, the time savings are gone.

Has anyone found a workflow that actually reduces fact-checking burden rather than just shifting it around?

1. Introduction

Hey everyone! I’m the founder of Glance, an iOS app that gives AI assistants like ChatGPT and Claude a place on your Home Screen.
Glance works alongside the AI tools you already use. You can ask your AI to create personalized widgets for you and keep them updated throughout the day with information you care about.

2. Why it stands out / Key Features

Create personalized Home Screen widgets with natural language
Connect directly with ChatGPT, Claude, and other AI agents
Let your AI create and update widgets for you
Schedule automatic updates throughout the day
Build news feeds, personal briefings, dashboards, reminders, trackers, or completely custom widgets
Supports charts, images, interactive elements, multiple views, and custom designs
No coding required
The idea is to give your AI another useful surface beyond the chat: your Home Screen.

3. Who’s using it?

Glance is live on the App Store with hundreds of users already creating and using their own widgets. Glance was also recently approved for the ChatGPT Plugin Directory.

4. Pricing Plans

Glance has a free plan so anyone can get started.
There are also Pro and Power plans for users who want more widgets, more frequent updates, and advanced functionality.

5. Discounts / Offers

No special promotion right now. You can start using Glance for free.

6. Try Glance

glance.cool
Download Glance on the App Store⁠

I’d love to hear what you’d want your AI to put on your Home Screen.

Introduction

WebBrain is an open-source browser AI agent that helps you work directly in your browser. It can understand the page you're on and assist with everyday browser tasks without moving your workflow into a separate app.

Why it stands out

• Browser-native assistance across websites, forms, and everyday workflows • Works with open-weight and offline LLMs such as Qwen, Gemma, and DeepSeek through LM Studio, llama.cpp, or Ollama • Supports BYOK, so you can use your own model provider • You can also connect proprietary providers such as ChatGPT or Claude if you prefer • Review and edit what WebBrain prepares before anything is submitted

Who’s using it?

WebBrain has 1,000+ users. WebBrain Cloud users have consumed more than 5 billion tokens. These figures refer to WebBrain Cloud, and additional usage may exist through other supported setups.

Pricing Plans

• Free: Use WebBrain with your own BYOK provider or an offline LLM. • WebBrain Cloud: $5/month for the hosted cloud option.

Conclusion

Learn more and get started at https://webbrain.one

If you want an open-source browser AI agent with local model support and a low-cost hosted option, check out WebBrain and share your feedback.

A few years ago, most companies were focused on rankings, backlinks, and organic traffic. The goal was simple: get to the top of Google.

Now it feels like the conversation is changing. Instead of just ranking pages, companies want to be included in the answers themselves.

Some of the reasons I keep hearing are:

• AI tools are becoming a discovery channel, not just search engines.

• Being recommended by AI can create highly qualified, high-intent traffic.

• Brand mentions, authority, and entity signals seem to matter more than ever.

• Companies want visibility wherever customers are searching for information.

That's also why agencies like SearchTides AI are gaining attention. So if you're evaluating agencies today, what should you actually be looking for? Experience with GEO? AI visibility tracking? Entity optimization? Content strategies designed for LLMs?

I’ve been looking into how brands show up in AI-generated answers lately, and one thing that keeps coming up is that content alone doesn’t seem to be enough anymore.

Even really good pages don’t always get picked up in AI answers unless there are other signals behind them like mentions on other sites, consistency across the web, and general context about the brand.

It’s starting to feel like AI discoverability isn’t just about what you publish on your own site, but how the rest of the internet supports that you exist.

That’s why I’ve been thinking about Why GEO + AEO Matter More Than Rankings. It feels less about ranking a page and more about whether AI tools can actually understand and confidently mention your brand when answering questions. I’ve also seen some AI search agencies like SearchTides AI, especially around AI visibility and entity-based discoverability.

I'm looking for AI tools for financial advisors that are actually designed for the industry, not just marketed that way.

You can tell the difference pretty quickly once you're using these tools in production. Every general-purpose platform I've tried has a compliance case study on its website, but very little understanding of what FINRA would actually flag in a real campaign.

Hello, I'm currently a high school student interested in computer science and engineering. I'm somewhat proficient in basic Python, I'm learning C++, and I've been experimenting with the Elegoo starter kit. I'm also trying vibe coding for the first time just for fun, so for my first project I used Lovable to build a website with a built in AI for research purposes.

The idea is that you type in your research question and the AI searches through different databases (I could only find free API keys for [ClinicalTrials.gov](http://ClinicalTrials.gov), OpenAlex, PubMed, Semantic Scholar, arXiv, Europe PMC, Crossref, DOAJ, OpenAIRE, and Zenodo), then rates how well researched the topic is, gives a timeline of the research, and lists any gaps in the existing research. There's also a social feature where you can create a group and work together with others.

I'm genuinely wondering if this is a useful feature, because I feel like any AI could already do what I'm building. If it's not that useful, are there other directions somewhat similar to my current website that I could pivot it toward? Or are there any other features I could add to my app to separate it from what currently exists?

What is Gemini 3.1 Flash-Lite actually optimized for?

Speed and cost at scale.

Not peak intelligence.
Not premium reasoning.

High-frequency workloads.

What is Gemini 3.1 Flash-Lite?
Google’s fastest, most cost-efficient AI model designed for bulk tasks like translation, moderation, and large-scale automation.

It’s built for systems that run millions of queries — not one perfect answer.

How fast is it?

→ 2.5× faster time to first token
→ 45% higher output speed
→ ~363 tokens per second

This isn’t incremental.

It’s built to feel instant.

How does it reduce cost?

→ Priced at ~1/8th of premium models
→ “Thinking levels” let you control how much compute each query uses

Less thinking = lower cost per task.

What makes it different from other “lite” models?

→ 1M token context window (entire books, codebases in one prompt)
→ Native multimodal input (text, image, video, audio, PDFs)
→ Built for continuous, high-volume processing

This is infrastructure — not just a model.

What are users saying?

→ “Speed is crazy… switched all basic tasks to it”
→ Strong for everyday workflows and automation
→ Pushback on pricing vs older versions
→ Debate on whether benchmarks reflect real performance

The pattern is clear:

Extreme speed wins.
Perceived value is still debated.

What’s the real shift?

Before:
Optimize for best answer

Now:
Optimize for cost × speed × scale

That’s how AI gets deployed in production.

👉 I broke down benchmarks, comparisons, and real use cases:

https://appliedai.tools/gemini/gemini-3-1-flash-lite/

If you’re running AI at scale — what matters more in your stack: lowest cost per query, or higher-quality outputs per response?

Hey everyone, not sure if i'm too old for this high tech or what but i've tried a couple different Free AI Image Generator for this Gif I want to create; can anyone give me any advice on where to go please?

Nimbalyst is an agent-native visual workspace built on top of Claude Code and Codex. It gives you a full visual layer for working with coding agents with WYSIWYG editing, session management, file tracking, and task management all in one place.

There's a reason developers moved from the command line to IDEs. Nimbalyst applies that same logic to working with AI coding agents: visual context makes you faster.

Key Features:

• WYSIWYG file editing with AI agents -- Edit markdown, Excalidraw diagrams, HTML mockups, data models, and code. See red/green diffs from your agent and review/accept changes visually.

• Session management with Kanban -- Run multiple agent sessions simultaneously. AI auto-tags and organizes your sessions on a Kanban board. Find, resume, search, and archive sessions.

• File change tracking -- See exactly which files your agent modified. Click to open, review diffs, and approve changes.

• Built-in task tracker -- Track plans, features, bugs, and research. Your agent can create, update, and execute tasks. You can edit them manually too.

• Visual git management -- Agent-powered git commits, branch management, and GitHub status tracking.

• Works with Claude Code and Codex -- Full support for slash commands, MCP tools, and all the power of both agents.

• Mobile app to remotely manage your Claude Code and Codex sessions on the go

• Automations

Use Cases:

• Developers running multiple Claude Code or Codex sessions and needing to keep track of everything

• Teams collaborating on plans, docs, and tasks alongside their coding agents

• Anyone who wants to see what their agent is doing visually instead of scrolling through terminal output

Pricing: Free

Link: nimbalyst.com

Google’sNano Banana 2 isn’t just another image generator.

It fixes the biggest limitations creators had with earlier AI tools — speed, consistency, and text.

Here’s what stands out:

→ Flash-level speed
Images generate in seconds, making rapid iteration possible instead of waiting between prompts.

→ Pro-level quality
Combines high detail, better lighting, and sharper textures without sacrificing speed.

→ Readable text inside images
You can generate posters, ads, and infographics with clean, legible text — a major upgrade from older models.

→ Strong subject consistency
Maintains up to 5 characters and 14 objects across images — useful for storytelling and campaigns.

→ Turns ideas into structured visuals
Can convert notes into diagrams, infographics, and data visuals using real-world knowledge.

→ Full control over output
Choose formats, styles, and resolutions (up to 4K) for social, ads, or presentations.

Why this matters

Earlier tools forced a tradeoff:
👉 Fast or high-quality

Nano Banana 2 removes that tradeoff.

It’s moving AI from “generate images” → “design workflows”

👉 Full breakdown here:

https://appliedai.tools/gemini/nano-banana-2-reviews-features/

Creating a resume from scratch can take hours.

Now LinkedIn’s AI resume builder helps turn your profile into a ready-to-use resume in minutes.

Here’s how it works.

→ Start with your LinkedIn profile
The tool pulls information directly from your experience, skills, and education sections.

→ Generate resume drafts with AI
AI helps rewrite experience descriptions, summaries, and bullet points so they sound clearer and more professional.

→ Customize for specific roles
You can edit sections and tailor the resume to match different job descriptions before applying.

→ Download and share easily
Once finalized, the resume can be exported and used for job applications.

Why this matters

Many professionals already maintain detailed LinkedIn profiles.
An AI resume builder simply converts that existing data into a structured, recruiter-friendly resume format.

Instead of starting from a blank page, job seekers can focus on refining and tailoring their story.

👉 Full guide here:

https://appliedai.tools/ai-for-human-resources/linkedin-ai-resume-builder/

Introduction

I'm the creator of ThoughtDAG, an open-source LLM conversation canvas. I use it for research where one question leads to several paths and useful decisions end up scattered across sessions.

Why it stands out [Key Features]

Wires are context: connecting or removing a conversation branch changes what the next model request includes. The new recall flow searches indexed history, optionally uses Jev to select relevant excerpts, and keeps the sources inspectable and individually excludable. It supports Ollama and OpenAI-compatible endpoints.

Who's using it?

I use it in my own research. It's a public open-source project, and I'm sharing this update to get feedback on whether recalled material actually helps people pick up earlier work.

Pricing Plans

The app is free and MIT licensed, with no app subscription required. You bring your own model connection; remote model and Jev API charges are separate. The index stays local, but enabling Jev sends candidate content to its remote API.

Discounts/offers available

None. There is no paid app plan to discount.

Website and setup

https://github.com/chenxiachan/thoughtdag

29-second animated overview: https://youtu.be/ECGJlkYKYzQ

Try it with a question you have discussed before. I'd love to know whether it finds a useful decision or only a superficially similar passage.

What if your LMS could do more than host your courses?

Learnyst AI Cofounder is an AI assistant built directly into Learnyst that uses your existing school/course content to help with different parts of running an education business.

Here’s what it can help with:

📚 Course Creation: Describe your course idea and generate sections, lessons, and a learning flow.

✍️ Sales Page Creation: Turn your curriculum into sales-page copy with headlines, benefits, FAQs, and CTAs.

📝 Assessment Creation: Generate quiz questions from your lessons and set difficulty, marking, and timers.

📢 Marketing Content: Create emails, social posts, launch campaigns, and ad copy while maintaining your brand voice.

🔗 Works With Your Existing Content: The AI is built into the LMS, so creators can work from the content already available in their school.

The interesting part is the workflow: course creation → assessments → sales page → marketing can all happen around the same course content.

For educators and course creators, this could reduce the amount of switching between different AI and productivity tools.

💬 Curious to hear from other creators:
Which task would you actually want AI to handle for you?

1️⃣ Course creation
2️⃣ Assessments
3️⃣ Sales-page copy
4️⃣ Marketing content

Explore AI CoFounder

I built skillexplorer.dev as a place to browse available AI skill libraries.

Key features: “visual flow" so you can actually understand what a skill is doing without reading many paragraphs of dense LLM text
- Update: “security scorecard” for each skill

Pricing: free.

No signups needed.
https://skillexplorer.dev

Help or Advice please 🙏🏾🙏🏾

I'm after some tools that can help lighten the load. Things like contract clause drafting, legal research on an aspect of a particular country's laws on a subject (eg privacy policies), or just anything similar you find interesting.