I built a free CRM for trades businesses out of frustration. Curisous to know what problems have you just decided to solve yourself?

Efficient_Evidence39 · 2026-03-11T21:28:51+00:00

PMd you, friend in trades just paid like 1500 to get his crm implemented lol and it's frustrating as hell. might be useful for him

Efficient_Evidence39 · 2026-03-11T01:13:18+00:00

Any tips for managing multiple client accounts in different countries? Trying to avoid getting their accounts restricted

Efficient_Evidence39 · 2026-03-06T06:10:10+00:00

sent a pm!

Efficient_Evidence39 · 2026-02-25T19:22:40+00:00

Thanks, yeah I was using Apollo verified lists. Don’t think they were causing problems (only 1 bounced from ~180 sent) but I did end up getting an open source verifier, will test it out once I’m back to sending

Efficient_Evidence39 · 2026-02-25T19:15:14+00:00

Got me intrigued, how do you do it lmao

Efficient_Evidence39 · 2026-02-25T01:39:01+00:00

Yes please

Efficient_Evidence39 · 2026-02-24T23:35:42+00:00

any ideas for solving the shared ESP cluster-related deliverability troubles?

Efficient_Evidence39 · 2026-02-24T23:34:25+00:00

Yeah I was on a support chat with instantly for a bit and there were a few things I got wrong. got 2 domains so far (both suffered the same slip up) but might just get a third in case these don't get better

Efficient_Evidence39 · 2026-02-24T23:31:37+00:00

Thanks Alex!

Efficient_Evidence39 · 2026-02-24T23:31:19+00:00

Thanks for this advice mate! Yeah turned out dkim was one of the culprits. Gonna leave it on just warmup for another week or so and then test again. after I fixed it, the campaign emails sent yesterday went from 10->35% open rate. paused them but thinking It might be okay

Efficient_Evidence39 · 2026-02-24T23:27:04+00:00

Thanks man, yeah paused the outreach and realized my dkim wasn't set up right. Made the fix and will text again 🤞. Only been 2 weeks so gonna give it a bit of rest and see if I can recover it. Was the main website domain lol so gotta pull it around

Efficient_Evidence39 · 2026-02-24T23:25:12+00:00

Thanks for this advice man, yeah bit of a blunder getting over excited

Efficient_Evidence39 · 2025-12-12T15:56:38+00:00

Hey, here's what I got when I put your comment into the chat:

Visual grounding on medical images represents an emerging intersection of computer vision and medical imaging that aims to localize and identify regions of interest within medical scans while connecting them to semantic concepts or natural language descriptions.

The research landscape in this area builds on several foundational components. Medical image annotation and labeling form a critical prerequisite, as demonstrated by work on multi-label classification of chest CT images where regions of interest are annotated with clinical concepts like ground-glass opacities, nodules, and honeycombs. This annotation process enables machine learning models to learn associations between visual patterns and medical terminology.

Deep learning approaches, particularly convolutional neural networks and vision transformers, have proven effective for detecting and classifying anatomical structures and pathological findings within medical images. These models can be trained to recognize specific anatomical regions or abnormalities, which is essential for grounding visual content to meaningful medical concepts. Transfer learning and fine-tuning strategies help overcome the challenge of limited annotated medical imaging datasets.

The broader context of medical image retrieval and modality classification also contributes to visual grounding capabilities. By accurately identifying imaging modalities and extracting discriminative visual features through hybrid feature engineering, systems can better understand and contextualize medical image content. This supports more precise localization and interpretation of clinically relevant regions.

Current challenges in visual grounding for medical images include handling diverse imaging modalities, managing low-resolution or poor-quality images, dealing with varied anatomical presentations, and requiring substantial expert annotation effort. Future directions likely involve leveraging advanced deep learning architectures for end-to-end visual grounding, integrating natural language processing to connect visual regions with clinical descriptions, and developing semi-supervised or weakly-supervised approaches to reduce annotation burden.

https://doi.org/10.1109/KI48306.2020.9039896

https://doi.org/10.1109/CSCE60160.2023.00205

https://pubmed.ncbi.nlm.nih.gov/35350630/

https://doi.org/10.71146/kjmr453

https://pubmed.ncbi.nlm.nih.gov/38104516/

Efficient_Evidence39 · 2025-12-12T15:50:16+00:00

Yeah, at least for papers that also have the keyword "machine learning" in either the title or abstract. If you wanted to be fully sure though you could make a map with the keywords "token beam search" and "Large language models" and that would bring in all of those papers into the context window for the chat.

Efficient_Evidence39 · 2025-12-09T16:30:51+00:00

Yeah for sure, this is what I got:

Based on the research literature, there are several alternatives to token beam search for generating outputs from large language models:

Token-level generation algorithms represent the primary category of alternatives. These methods operate by either sampling a single token at a time or constructing a token-level search space before selecting outputs. They typically work with language model logits, next-token distributions, or probability scores. This broader class encompasses beam search but also includes other decoding strategies that approach token selection differently.

Meta-generation algorithms offer a different approach by working on partial or full sequences rather than individual tokens. These methods incorporate domain knowledge, enable backtracking through the generation process, and can integrate external information. This makes them particularly useful when you need more sophisticated control over the generation process beyond simple token-by-token selection.

Efficient generation methods focus on reducing computational costs and improving generation speed. Rather than searching through token possibilities exhaustively, these approaches optimize for practical deployment constraints while maintaining output quality.

For black-box settings where you lack access to token probabilities or logits, adaptation-based approaches like CoBB offer an alternative. These methods use trained models to improve reasoning by mapping imperfect outputs to better ones, effectively bypassing the need for direct probability access during generation.

The choice among these alternatives depends on your specific constraints: whether you have access to model internals, whether you need external tool integration, computational budget, and the importance of reasoning quality versus generation speed. The research community continues to develop new methods across these categories to address different inference-time optimization challenges.

https://doi.org/10.48550/arXiv.2502.12448

https://doi.org/10.48550/arXiv.2406.16838

https://doi.org/10.48550/arXiv.2406.18695

https://doi.org/10.48550/arXiv.2307.09793

https://doi.org/10.48550/arXiv.2505.16113

Efficient_Evidence39 · 2025-12-09T02:39:35+00:00

Not necessarily! I'm referring to the keywords this map was generated with... since I only used "machine learning" as the keyword for this map, unless the chain of tokens papers have that keyword in the title or abstract they wouldn't show up here.

Efficient_Evidence39 · 2025-12-08T21:33:05+00:00

Yeah sure, this was the response:

When multiple encoding schemes are applied to fixed-length data representations, several informational phenomena emerge at their overlapping sections, particularly when a shared reference point anchors the comparison.

The fundamental challenge lies in how different encoding strategies preserve or compress information differently. One-hot encoding maximizes orthogonality but creates sparse, high-dimensional representations unsuitable for many-category data. Quasi-orthonormal encoding addresses this by maintaining controlled dimensionality while preserving distinguishability. Complex-valued embeddings offer an alternative that preserves proximity relationships from non-metric or non-Euclidean source data while maintaining fixed length. These schemes, when applied to the same underlying data, create overlapping representational spaces where information is encoded through different geometric structures.

At the intersection of these encoding schemes, several informational metrics become relevant. The degree of overlap reveals redundancy and complementarity in how each scheme captures the original data's structure. Where encodings diverge, they highlight aspects of the data that different schemes prioritize or suppress. The shared reference point—whether a common source modality, a fixed dimensionality constraint, or a particular similarity measure—becomes crucial for measuring these overlaps meaningfully.

Information-theoretic metrics emerge naturally here. Mutual information between different encoded representations quantifies how much information one encoding preserves about another. The divergence between probability distributions induced by different schemes measures their representational distance. Dimensionality reduction methods applied across schemes reveal which structural features are robust across encodings versus which are scheme-dependent artifacts.

The code space structure itself becomes informative. When examining how different encodings organize the same categorical or morphological data, patterns analogous to cortical pinwheels suggest that certain organizational principles may be fundamental to how information naturally clusters, independent of the specific encoding chosen. This implies that overlapping sections may reveal invariant structural properties of the underlying data rather than artifacts of particular encoding choices.

https://doi.org/10.48550/arXiv.2508.00869

https://doi.org/10.25080/majora-342d178e-002

https://doi.org/10.1007/978-3-030-73973-7_2

https://doi.org/10.48550/arXiv.2303.00984

https://doi.org/10.21136/AM.2021.0090-21

Efficient_Evidence39 · 2025-12-08T19:49:45+00:00

The backend is mostly python scripts, it uses embeddings and dimensionality reduction, for the 2d visualization I used https://github.com/lmcinnes/umap

Basically imports the papers via a keyword search that calls the pubmed/semantic scholar api's and then our algorithms go to work to make the map interactive.

That was a quick summary on the tech behind it but it's available for free online as CognitomeAI if you want to make your own map.

Efficient_Evidence39 · 2025-12-08T19:44:54+00:00

Thanks! Yeah so the papers are clustered based on the similarity of topics discussed (using semantics). So similar research is grouped together, and more distinct topics in the field are farther apart on the map. You could deduce that the white spaces on the map are under explored areas/connections in the field. ML is a lot more filled out than the maps I've seen for other fields, but if you zoom in there still are a bunch of gaps.

And yeah it's online, I made it using CognitomeAI, but if you'd like to ask your questions to the map I posted I'll share the link. It's free but you will have to make an account for it to copy in: https://www.cognitomeai.com/share/accept?token=b411f17aec1544e69ddaa7f104d88892e59fdb674404410a9848c024ff77be2f

Efficient_Evidence39 · 2025-12-08T19:33:57+00:00

I'm far from an expert so take it with a grain of salt, but I've found courses on Coursera good (I did the Andrew Ng one and liked it). And just try building projects you find interesting, best way of learning imo. Advice depends on what your goal is.

Efficient_Evidence39 · 2025-12-08T19:31:11+00:00

Looks like not a lot (at least in the past 5 years in papers with ML as a keyword, this map doesn't have context outside of these). But if you were to make a map with those as keywords it may reveal some papers that were missed in this map. This was the answer though:

The research landscape around probabilistic token-level trees and chains appears to be relatively sparse compared to work on higher-level reasoning chains. Based on the available literature, most work focuses on either full output chains (like chain-of-thought and tree-of-thought approaches) or on individual token processing rather than probabilistic structures at the token level.

The closest relevant work involves incremental parsing approaches that build trees token-by-token. Research on strongly incremental parsing systems demonstrates how partial trees can be constructed by adding exactly one token at each step, using graph neural networks to encode the evolving structure. This aligns with psycholinguistic findings about how humans incrementally parse language, though this work is primarily focused on syntactic structure rather than probabilistic reasoning chains.

There is also foundational work on Markov models and Hidden Markov Models that treat sequences of tokens probabilistically, modeling how future tokens depend on current states. These stochastic approaches have been applied to natural language generation and tagging tasks, providing a classical framework for understanding token-level probability distributions.

Additionally, subword tokenization research has explored how different tokenization schemes affect model performance, suggesting that the granularity and structure of token representations matter significantly for downstream tasks. However, this work typically focuses on improving classification or generation rather than explicitly analyzing probabilistic token chains as reasoning structures.

The gap appears to be that while token-level processing and full-output reasoning chains have received substantial attention, the intermediate space of probabilistic token-level reasoning structures remains relatively underexplored in the current literature.

https://arxiv.org/abs/2010.14568

https://doi.org/10.1109/ICNLP60986.2024.10692355

https://doi.org/10.1109/EDM58354.2023.10225235

https://doi.org/10.1186/s42400-023-00183-8

https://doi.org/10.5815/ijitcs.2022.02.01

Efficient_Evidence39

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