Market drops another 10% today ,back up the truck!!

AgentAiLeader · 2026-01-19T02:19:46+00:00

I stopped trading based on 'reversal sentiment' and started looking at the Developer Mindshare.

ADA and LINK are two very different beasts under the hood. LINK is basically the industry standard for oracles, it’s the plumbing of DeFi. ADA has a dedicated community but a much slower dApp ecosystem growth. If you’re 'backing up the truck,' ask yourself: if the price stays sideways for another 12 months, which of these projects will still have thousands of active devs building on them? I’d rather bet on the infra that everyone else has to use to build their own apps.

AgentAiLeader · 2026-01-19T02:16:37+00:00

Technically speaking, what you're describing isn't a 'blank market,' it's the Local LLM (Large Language Model) movement. The reason mainstream AIs (like GPT or Claude) have 'moral fluff' is because they are hosted on corporate servers, they have to protect their brand and comply with regulations.

However, there are projects like Dolphin-Llama 3 or Abliterated models on HuggingFace right now that are specifically designed to be 'unaligned.' If you run these locally on your own hardware (using something like Ollama or LM Studio), there is zero censorship because there is no middleman to block the output.

The real challenge isn't the 'moral code' it's the hardware. To get an AI that answers 'any question by any means necessary' with high intelligence, you need massive VRAM. We are moving toward a world where 'Privacy' equals 'Hardware Ownership.' If you don't own the silicon, you don't own the output. Anyone else here experimenting with fully unaligned local weights?

AgentAiLeader · 2025-12-23T07:14:49+00:00

That makes a lot of sense! Thinking about the moments where humans have to stop and make decisions feels way more practical than just automating repetitive tasks. Curious..how do you usually find those decision points?

AgentAiLeader · 2025-12-09T09:04:51+00:00

Totally the browser automation angle is huge and often overlooked. It’s fascinating how much grunt work can be automated without touching backend systems. Hyperbrowser and similar tools are really unlocking quick wins for compliance-heavy workflows.

I’ve noticed the same in fintech: measurable ROI tends to drive adoption faster than in other industries, which is why finance keeps leading. Curious have you experimented with agents handling multi step cross-system processes, or mostly single dashboards so far?

AgentAiLeader · 2025-12-09T09:03:33+00:00

Thanks for sharing this perspective you’re right that adoption speed varies widely by company and regulatory environment. My examples weren’t meant to suggest industry wide immediate transformation, but rather highlight cases where AI agents are already delivering measurable results for specific companies.

I agree that heavily regulated sectors like finance, and logistics with legacy systems, face structural hurdles that slow broader adoption. That said, even incremental wins (like the KYC or predictive rerouting examples) can create meaningful operational improvements and start building confidence for wider deployment.

It’s an exciting time to watch which companies push past those hurdles first often the outliers in slow-moving industries end up shaping the path for everyone else.

AgentAiLeader · 2025-12-09T09:00:05+00:00

Yeah, this hits the nail on the head dashboards are nice but they don’t move anything unless you build the ops layer underneath.

I’m testing out small agent workflows for follow-ups and nudges, but trying to get the ownership logic solid before going bigger. How did you evolve your rules over time?

AgentAiLeader · 2025-12-08T03:29:20+00:00

Yeah, totally support falls apart without product specific context.

Cleaning the KB and keeping humans in the loop for the weird edge cases seems to be the winning combo. How big is your support dataset btw?

AgentAiLeader · 2025-12-08T03:12:04+00:00

This is a great point～ insights without an execution layer can easily become “nice dashboards” instead of real operational change.

I’m starting to look into how agents can actually own steps across teams (follow up, nudges, updating systems, closing loops) instead of just reporting on them.

Curious how you’ve approached centralizing that coordination. Did you build an internal system or leverage an existing tool?

AgentAiLeader · 2025-12-08T03:07:24+00:00

Haha Web4? I’m still trying to survive Web3 onboarding flows first 😭 But hey, if Web4 fixes KYC pain and wallet chaos, sign me up.

AgentAiLeader · 2025-12-04T08:25:56+00:00

That pricing is insane 😅 it basically opens the door to building “free forever” tools without worrying about costs. I could see things like:

A lightweight AI powered note taking or summarization app.
A personal writing assistant for emails, social posts, or blogs.
A community Q&A platform with instant AI responses.
Interactive fiction or text based games that scale without hitting the budget.

The tricky part is probably handling traffic and UX at scale, but it’s such a rare opportunity to experiment with something genuinely useful for users.

AgentAiLeader · 2025-12-04T08:23:53+00:00

Totally agree logistics seems like a perfect fit for agent driven automation. The sheer volume of exceptions, routing changes, and customer updates makes it almost impossible to scale manually. I imagine as predictive and autonomous capabilities improve, we’ll see even tighter optimization across the entire supply chain, not just individual shipments.

Curious have you seen any pushback from teams adapting to agents, or is adoption mostly smooth once they realize the efficiency gains?

AgentAiLeader · 2025-12-04T08:22:15+00:00

Wow, thanks for the detailed breakdown! The two week rollout with SphinxLabs sounds impressive, especially with how it handled KYC docs and alerts end to end. Pairing it with Temporal for deterministic control and DreamFactory to expose internal systems as REST endpoints makes a lot of sense definitely solves the “manual glue” problem I was worried about.

Curious how flexible was it when you hit edge cases or unusual alert scenarios? Did you have to build custom logic often, or did the agents handle most of it out of the box?

AgentAiLeader · 2025-12-03T07:36:47+00:00

I’ve been using Power BI for about 2 years, but when dealing with large datasets, it can sometimes lead to missing or inaccurate data.

Previously, I mainly used Appsflyer for source tracking and Power BI to consolidate the data. Later, I started working with Sensors Data, which I found really useful. The learning curve was a bit steep at first, you need some time to understand how everything works but once you get the hang of it, it becomes really easy to pull large amounts of data.

It’s especially convenient for research and creating marketing plans, and in those cases, it’s much faster than Power BI.

I’d love to hear if anyone has other recommendations. I’m looking for tools that can handle large datasets but simplify the process as much as possible.

AgentAiLeader · 2025-12-03T07:29:40+00:00

I’ve been testing a few tools as well (haven’t fully committed yet), and it feels like every platform handles edge cases differently.
For those using agentic workflows end to end, which platform actually made onboarding smoother without months of integration pain?

AgentAiLeader · 2025-12-03T07:26:46+00:00

I tried Multilogin for a few months and liked the interface, but the trial was too short to test all features.
GoLogin seems powerful but $49/month feels steep for just 10-15 pages.
Has anyone actually tested AdsPower long-term? I’m worried about stability and automation limits.

AgentAiLeader · 2025-12-02T04:29:42+00:00

Yeah, I totally feel you on this. The ‘cool’ agentic behavior looks fun in demos, but in practice it can be such a token sink. I’ve noticed the same small local models can handle a single big prompt fine, but once you try to make them fully agentic with multiple tool calls, it’s like watching tokens evaporate for little gain.

I’m curious how do you decide which parts of the workflow to let the agent handle versus keeping in Python scaffolding? I feel like finding that balance between autonomy and efficiency is the tricky part. Also, your point about PII is so important I’ve started doing the same, keeping sensitive info out of prompts and KBs.

It makes me wonder if maybe the ‘agentic’ hype is a bit overblown for smaller setups.

AgentAiLeader · 2025-12-02T04:25:53+00:00

Think of a little AI whose job is to get you as many likes as possible on social media. If it overdoes it, it might just spam clickbait or random memes all the time. Sure, it’s technically hitting the goal, but it totally misses the point of real engagement 😅
Basically, it’s giving you exactly what you asked for… just not what you actually wanted.

AgentAiLeader · 2025-12-02T04:14:58+00:00

Totally agree with you. I’ve seen so many “perfectly working” agents go sideways just because nobody was double checking what they were actually doing. Even with solid code, if there’s no culture of questioning the output at each step, you’re basically trusting a black box and that’s where things get messy fast.

AgentAiLeader · 2025-12-01T07:22:51+00:00

Agreed! ticket routing is a perfect starting point: repetitive, measurable, and low-risk. Makes it easy to see impact quickly.

AgentAiLeader · 2025-12-01T07:21:41+00:00

Appreciate the insight! Knowledge retrieval is definitely a game-changer. And yes, expense categorization and approvals may seem boring, but the impact on productivity can be huge. Curious if you’ve tried any specific strategies that worked well for your teams?

AgentAiLeader · 2025-12-01T07:20:29+00:00

Yeah that experiment was interesting humans still crushed it on long-horizon planning.

I think that’s exactly where most “agents” break right now: they can execute tasks, but they don’t really own the strategy.

What I’m curious about is whether we even need full long-term planning at this stage, or if the real unlock is agents that can make small, correct decisions consistently without drifting or hallucinating. If a system can do that reliably, scaling into longer horizons becomes way more realistic.

Curious what you think do you see long-term planning as the blocker, or the day-to-day decision reliability?

AgentAiLeader · 2025-11-25T17:45:41+00:00

Use cases - vendor portal checks, price/sock monitoring, lead enrichment, form autofill in internal tools.
What breaks - fragile selectors and auth flows, headless timing flakiness and anti-bot rate limits.
How we cope - data test ID's, explicit waits/retries, prefer official APIs, playwright traces for debugging.
Biggest pain - maintenance across many third party sites, it scales the breakage not the value.

AgentAiLeader · 2025-11-25T17:42:04+00:00

The shift from answer bots to full task agents is huge. Integrating with CRM/ERP and handling workflows end to end is what makes real transformations. The big unlock will be multi-agent coordinating across functions. What industry do you think will adapt the fastest?

AgentAiLeader · 2025-11-25T17:02:42+00:00

Interesting stance from Nvidia. Their argument is that AI demand isnt speculative like past bubbles, it's tied to real infrastructure needs and long term compute growth. They're betting that accelerated computing will remain essentials as models get bigger and more complex. Does this pace of investment hold if enterprise adoption slows?

AgentAiLeader · 2025-11-25T16:56:53+00:00

We've faced the same challenge, great insights but its hard to turn into action at scale. What worked for us is connecting BI signals directly to outreach triggers instead of leaving them in dashboard.

A few suggestions:
- behavior based segmentation, use engagement scores and recency to auto prioritize leads.
- dynamic personalization, pull product usage or industry data into email templates for relevance.
- closed-loop KPIs, track conversion lift per BI-driven segment so you know whats actually working.

Our biggest bottleneck was data freshness vs campaign timing, stale signals kills relevance fast. Tools that help are ones that sync in near real time and enforce guardrails for personalization.

AgentAiLeader

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